Showing papers on "Matching (statistics) published in 2011"
TL;DR: The propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects, and different causal average treatment effects and their relationship with propensity score analyses are described.
Abstract: The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
7,895 citations
TL;DR: An extensive series of Monte Carlo simulations were conducted to determine the optimal caliper width for estimating differences in means (for continuous outcomes) and risk differences (for binary outcomes).
Abstract: In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates. Propensity-score matching is increasingly being used to estimate the effects of exposures using observational data. In the most common implementation of propensity-score matching, pairs of treated and untreated subjects are formed whose propensity scores differ by at most a pre-specified amount (the caliper width). There has been a little research into the optimal caliper width. We conducted an extensive series of Monte Carlo simulations to determine the optimal caliper width for estimating differences in means (for continuous outcomes) and risk differences (for binary outcomes). When estimating differences in means or risk differences, we recommend that researchers match on the logit of the propensity score using calipers of width equal to 0.2 of the standard deviation of the logit of the propensity score. When at least some of the covariates were continuous, then either this value, or one close to it, minimized the mean square error of the resultant estimated treatment effect. It also eliminated at least 98% of the bias in the crude estimator, and it resulted in confidence intervals with approximately the correct coverage rates. Furthermore, the empirical type I error rate was approximately correct. When all of the covariates were binary, then the choice of caliper width had a much smaller impact on the performance of estimation of risk differences and differences in means. Copyright © 2010 John Wiley & Sons, Ltd.
2,538 citations
TL;DR: In this paper, the authors proposed a bias correction that renders matching estimators N 1/2-consistent and asymptotically normal, and applied the methods proposed in this article to the National Supported Work (NSW) data, originally analyzed in Lalonde (1986).
Abstract: In Abadie and Imbens (2006), it was shown that simple nearest-neighbor matching estimators include a conditional bias term that converges to zero at a rate that may be slower than N1/2. As a result, matching estimators are not N1/2-consistent in general. In this article, we propose a bias correction that renders matching estimators N1/2-consistent and asymptotically normal. To demonstrate the methods proposed in this article, we apply them to the National Supported Work (NSW) data, originally analyzed in Lalonde (1986). We also carry out a small simulation study based on the NSW example. In this simulation study, a simple implementation of the bias-corrected matching estimator performs well compared to both simple matching estimators and to regression estimators in terms of bias, root-mean-squared-error, and coverage rates. Software to compute the estimators proposed in this article is available on the authors’ web pages (http://www.economics.harvard.edu/faculty/imbens/software.html) and documented in Aba...
1,311 citations
TL;DR: Matching as mentioned in this paper is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm and a variety of univariate and multivariate metrics to determine if balance actually has been obtained.
Abstract: Matching is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance actually has been obtained are provided. The underlying matching algorithm is written in C++, makes extensive use of system BLAS and scales efficiently with dataset size. The genetic algorithm which finds optimal balance is parallelized and can make use of multiple CPUs or a cluster of computers. A large number of options are provided which control exactly how the matching is conducted and how balance is evaluated.
1,184 citations
TL;DR: A proper probabilistic model for the denoising autoencoder technique is defined, which makes it in principle possible to sample from them or rank examples by their energy, and a different way to apply score matching that is related to learning to denoise and does not require computing second derivatives is suggested.
Abstract: Denoising autoencoders have been previously shown to be competitive alternatives to restricted Boltzmann machines for unsupervised pretraining of each layer of a deep architecture. We show that a simple denoising autoencoder training criterion is equivalent to matching the score (with respect to the data) of a specific energy-based model to that of a nonparametric Parzen density estimator of the data. This yields several useful insights. It defines a proper probabilistic model for the denoising autoencoder technique, which makes it in principle possible to sample from them or rank examples by their energy. It suggests a different way to apply score matching that is related to learning to denoise and does not require computing second derivatives. It justifies the use of tied weights between the encoder and decoder and suggests ways to extend the success of denoising autoencoders to a larger family of energy-based models.
779 citations
20 Jun 2011
TL;DR: A novel Probabilistic Relative Distance Comparison (PRDC) model is introduced, which differs from most existing distance learning methods in that it aims to maximise the probability of a pair of true match having a smaller distance than that of a wrong match pair, which makes the model more tolerant to appearance changes and less susceptible to model over-fitting.
Abstract: Matching people across non-overlapping camera views, known as person re-identification, is challenging due to the lack of spatial and temporal constraints and large visual appearance changes caused by variations in view angle, lighting, background clutter and occlusion. To address these challenges, most previous approaches aim to extract visual features that are both distinctive and stable under appearance changes. However, most visual features and their combinations under realistic conditions are neither stable nor distinctive thus should not be used indiscriminately. In this paper, we propose to formulate person re-identification as a distance learning problem, which aims to learn the optimal distance that can maximises matching accuracy regardless the choice of representation. To that end, we introduce a novel Probabilistic Relative Distance Comparison (PRDC) model, which differs from most existing distance learning methods in that, rather than minimising intra-class variation whilst maximising intra-class variation, it aims to maximise the probability of a pair of true match having a smaller distance than that of a wrong match pair. This makes our model more tolerant to appearance changes and less susceptible to model over-fitting. Extensive experiments are carried out to demonstrate that 1) by formulating the person re-identification problem as a distance learning problem, notable improvement on matching accuracy can be obtained against conventional person re-identification techniques, which is particularly significant when the training sample size is small; and 2) our PRDC outperforms not only existing distance learning methods but also alternative learning methods based on boosting and learning to rank.
734 citations
TL;DR: In this paper, the authors introduce a new class of matching methods called Monotonic Imbalance Bounding (MIB), which generalizes and extends EPBR in several new directions.
Abstract: We introduce a new “Monotonic Imbalance Bounding” (MIB) class of matching methods for causal inference with a surprisingly large number of attractive statistical properties. MIB generalizes and extends in several new directions the only existing class, “Equal Percent Bias Reducing” (EPBR), which is designed to satisfy weaker properties and only in expectation. We also offer strategies to obtain specific members of the MIB class, and analyze in more detail a member of this class, called Coarsened Exact Matching, whose properties we analyze from this new perspective. We offer a variety of analytical results and numerical simulations that demonstrate how members of the MIB class can dramatically improve inferences relative to EPBR-based matching methods.
703 citations
01 Nov 2011
TL;DR: This paper presents a GPU-based stereo matching system with good performance in both accuracy and speed, and is the top performer in the Middlebury benchmark, and the results are achieved on GPU within 0.1 seconds.
Abstract: This paper presents a GPU-based stereo matching system with good performance in both accuracy and speed. The matching cost volume is initialized with an AD-Census measure, aggregated in dynamic cross-based regions, and updated in a scanline optimization framework to produce the disparity results. Various errors in the disparity results are effectively handled in a multi-step refinement process. Each stage of the system is designed with parallelism considerations such that the computations can be accelerated with CUDA implementations. Experimental results demonstrate the accuracy and the efficiency of the system: currently it is the top performer in the Middlebury benchmark, and the results are achieved on GPU within 0.1 seconds. We also provide extra examples on stereo video sequences and discuss the limitations of the system.
563 citations
06 Nov 2011
TL;DR: This paper derives a direct matching framework based on visual vocabulary quantization and a prioritized correspondence search that efficiently handles large datasets and outperforms current state-of-the-art methods.
Abstract: Recently developed Structure from Motion (SfM) reconstruction approaches enable the creation of large scale 3D models of urban scenes. These compact scene representations can then be used for accurate image-based localization, creating the need for localization approaches that are able to efficiently handle such large amounts of data. An important bottleneck is the computation of 2D-to-3D correspondences required for pose estimation. Current stateof- the-art approaches use indirect matching techniques to accelerate this search. In this paper we demonstrate that direct 2D-to-3D matching methods have a considerable potential for improving registration performance. We derive a direct matching framework based on visual vocabulary quantization and a prioritized correspondence search. Through extensive experiments, we show that our framework efficiently handles large datasets and outperforms current state-of-the-art methods.
522 citations
TL;DR: In this article, the problem of matching drivers and riders in a dynamic setting is considered, and optimization-based approaches are developed to minimize the total systemwide vehicle miles incurred by system users, and their individual travel costs.
Abstract: Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based approaches that aim at minimizing the total system-wide vehicle miles incurred by system users, and their individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers.
391 citations
TL;DR: LSM appears to reflect implicit interpersonal processes central to romantic relationships, and is associated with long-term commitment in dyads.
Abstract: Previous relationship research has largely ignored the importance of similarity in how people talk with one another. Using natural language samples, we investigated whether similarity in dyads’ use of function words, called language style matching (LSM), predicts outcomes for romantic relationships. In Study 1, greater LSM in transcripts of 40 speed dates predicted increased likelihood of mutual romantic interest (odds ratio = 3.05). Overall, 33.3% of pairs with LSM above the median mutually desired future contact, compared with 9.1% of pairs with LSM at or below the median. In Study 2, LSM in 86 couples’ instant messages positively predicted relationship stability at a 3-month follow-up (odds ratio = 1.95). Specifically, 76.7% of couples with LSM greater than the median were still dating at the follow-up, compared with 53.5% of couples with LSM at or below the median. LSM appears to reflect implicit interpersonal processes central to romantic relationships.
TL;DR: Compared to a leading commercial face recognition system, LFDA offers substantial improvements in matching forensic sketches to the corresponding face images and leads to state-of-the-art accuracys when matching viewed sketches.
Abstract: The problem of matching a forensic sketch to a gallery of mug shot images is addressed in this paper. Previous research in sketch matching only offered solutions to matching highly accurate sketches that were drawn while looking at the subject (viewed sketches). Forensic sketches differ from viewed sketches in that they are drawn by a police sketch artist using the description of the subject provided by an eyewitness. To identify forensic sketches, we present a framework called local feature-based discriminant analysis (LFDA). In LFDA, we individually represent both sketches and photos using SIFT feature descriptors and multiscale local binary patterns (MLBP). Multiple discriminant projections are then used on partitioned vectors of the feature-based representation for minimum distance matching. We apply this method to match a data set of 159 forensic sketches against a mug shot gallery containing 10,159 images. Compared to a leading commercial face recognition system, LFDA offers substantial improvements in matching forensic sketches to the corresponding face images. We were able to further improve the matching performance using race and gender information to reduce the target gallery size. Additional experiments demonstrate that the proposed framework leads to state-of-the-art accuracys when matching viewed sketches.
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07 Sep 2011TL;DR: Novel techniques are described for model-based recognition of 3-D objects from unknown viewpoints using single-gray-scale images and efficient matching algorithms are proposed, which assume affine approximation to the perspective viewing transformation.
Abstract: Novel techniques are described for model-based recognition of 3-D objects from unknown viewpoints using single-gray-scale images. The objects in the scene may be overlapping and partially occluded. Efficient matching algorithms, which assume affine approximation to the perspective viewing transformation, are proposed. The study is currently restricted to flat rigid 3-D objects. Point, line and curve matching algorithms are presented. The study especially emphasizes the curve matching problem. Experimental results are included. >
12 Dec 2011
TL;DR: A surprisingly simple method that estimates the relative importance of different features in a query image based on the notion of "data-driven uniqueness" is proposed, yielding a generic approach that does not depend on a particular image representation or a specific visual domain.
Abstract: The goal of this work is to find visually similar images even if they appear quite different at the raw pixel level This task is particularly important for matching images across visual domains, such as photos taken over different seasons or lighting conditions, paintings, hand-drawn sketches, etc We propose a surprisingly simple method that estimates the relative importance of different features in a query image based on the notion of "data-driven uniqueness" We employ standard tools from discriminative object detection in a novel way, yielding a generic approach that does not depend on a particular image representation or a specific visual domain Our approach shows good performance on a number of difficult cross-domain visual tasks eg, matching paintings or sketches to real photographs The method also allows us to demonstrate novel applications such as Internet re-photography, and painting2gps While at present the technique is too computationally intensive to be practical for interactive image retrieval, we hope that some of the ideas will eventually become applicable to that domain as well
TL;DR: The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy.
Abstract: Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Poor quality of ridge impressions, small finger area, and large nonlinear distortion are the main difficulties in latent fingerprint matching compared to plain or rolled fingerprint matching. We propose a system for matching latent fingerprints found at crime scenes to rolled fingerprints enrolled in law enforcement databases. In addition to minutiae, we also use extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map, and skeleton. We tested our system by matching 258 latents in the NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining the NIST SD4, SD14, and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9 percent was improved to 74 percent when extended features were used. In order to evaluate the relative importance of each extended feature, these features were incrementally used in the order of their cost in marking by latent experts. The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy.
TL;DR: It is argued that the motivation of diversity research is to increase the probability of retrieving unusual or novel items which are relevant to the user and a methodology to evaluate their performance in terms of novel item retrieval is introduced.
Abstract: For recommender systems that base their product rankings primarily on a measure of similarity between items and the user query, it can often happen that products on the recommendation list are highly similar to each other and lack diversity. In this article we argue that the motivation of diversity research is to increase the probability of retrieving unusual or novel items which are relevant to the user and introduce a methodology to evaluate their performance in terms of novel item retrieval. Moreover, noting that the retrieval of a set of items matching a user query is a common problem across many applications of information retrieval, we formulate the trade-off between diversity and matching quality as a binary optimization problem, with an input control parameter allowing explicit tuning of this trade-off. We study solution strategies to the optimization problem and demonstrate the importance of the control parameter in obtaining desired system performance. The methods are evaluated for collaborative recommendation using two datasets and case-based recommendation using a synthetic dataset constructed from the public-domain Travel dataset.
01 Dec 2011
TL;DR: In this article, the authors compare and evaluate different image matching methods for glacier flow determination over large scales, and they consider CCF-O and COSI-Corr to be the two most robust matching methods.
Abstract: Automatic matching of images from two different times is a method that is often used to derive glacier surface velocity. Nearly global repeat coverage of the Earth's surface by optical satellite sensors now opens the possibility for global-scale mapping and monitoring of glacier flow with a number of applications in, for example, glacier physics, glacier-related climate change and impact assessment, and glacier hazard management. The purpose of this study is to compare and evaluate different existing image matching methods for glacier flow determination over large scales. The study compares six different matching methods: normalized cross-correlation (NCC), the phase correlation algorithm used in the COSI-Corr software, and four other Fourier methods with different normalizations. We compare the methods over five regions of the world with different representative glacier characteristics: Karakoram, the European Alps, Alaska, Pine Island (Antarctica) and southwest Greenland. Landsat images are chosen for matching because they expand back to 1972, they cover large areas, and at the same time their spatial resolution is as good as 15 m for images after 1999 (ETM + pan). Cross-correlation on orientation images (CCF-O) outperforms the three similar Fourier methods, both in areas with high and low visual contrast. NCC experiences problems in areas with low visual contrast, areas with thin clouds or changing snow conditions between the images. CCF-O has problems on narrow outlet glaciers where small window sizes (about 16 pixels by 16 pixels or smaller) are needed, and it also obtains fewer correct matches than COSI-Corr in areas with low visual contrast. COSI-Corr has problems on narrow outlet glaciers and it obtains fewer correct matches compared to CCF-O when thin clouds cover the surface, or if one of the images contains snow dunes. In total, we consider CCF-O and COSI-Corr to be the two most robust matching methods for global-scale mapping and monitoring of glacier velocities. If combining CCF-O with locally adaptive template sizes and by filtering the matching results automatically by comparing the displacement matrix to its low pass filtered version, the matching process can be automated to a large degree. This allows the derivation of glacier velocities with minimal (but not without!) user interaction and hence also opens up the possibility of global-scale mapping and monitoring of glacier flow.
TL;DR: In this article, the authors examined the impact of non-farm work on household income and food security among farm households in the Northern Region of Ghana by employing propensity score matching method that accounts for self-selection bias.
TL;DR: This paper identified employees at seven companies whose 401(k) investment choices are dominated because they are contributing less than the employer matching contribution threshold despite being vested in their match and being able to make penalty-free withdrawals for any reason because they were older than 59 ½.
Abstract: We identify employees at seven companies whose 401(k) investment choices are dominated because they are contributing less than the employer matching contribution threshold despite being vested in their match and being able to make penalty-free 401(k) withdrawals for any reason because they are older than 59½. At the average firm, 36% of match-eligible employees over 59½ forego arbitrage profits that average 1.6% of their annual pay, or $507. A survey educating employees about the free lunch they are foregoing raised contribution rates by a statistically insignificant 0.67 percent of income among those completing the survey.
01 May 2011
TL;DR: This work proposes to use locally segmented contours combined with an implicit star-shaped object model as prototypes for the different sign classes in traffic sign recognition by using the correlation based matching scheme for Fourier descriptors and a fast cascaded match scheme for enforcing the spatial requirements.
Abstract: Traffic sign recognition is important for the development of driver assistance systems and fully autonomous vehicles. Even though GPS navigator systems works well for most of the time, there will always be situations when they fail. In these cases, robust vision based systems are required. Traffic signs are designed to have distinct colored fields separated by sharp boundaries. We propose to use locally segmented contours combined with an implicit star-shaped object model as prototypes for the different sign classes. The contours are described by Fourier descriptors. Matching of a query image to the sign prototype database is done by exhaustive search. This is done efficiently by using the correlation based matching scheme for Fourier descriptors and a fast cascaded matching scheme for enforcing the spatial requirements. We demonstrated on a publicly available database state of the art performance.
TL;DR: It is found that effective adaptive matching can be easily achieved by tracking the split resonant frequency of a wireless power transfer system, and a modified frequency tracking method is proposed to extend the range over which the power is transmitted with high efficiency.
Abstract: Adaptive matching methods for a wireless power transfer system in the near-field region are investigated. The impedance and resonant frequency characteristic of a near-field power transfer system are analyzed according to coupling distance. In the near-field region, adaptive matching is necessary to achieve an effective power transfer. We compare the power transfer efficiencies of several schemes including simultaneous conjugate matching and frequency tracking. It is found that effective adaptive matching can be easily achieved by tracking the split resonant frequency. In addition, a modified frequency tracking method is proposed to extend the range over which the power is transmitted with high efficiency. The experimental results are in agreement with the theoretical results.
TL;DR: The optimal nonbipartite matching algorithm and its statistical applications are reviewed, including observational studies with complex designs and an exact distribution-free test comparing two multivariate distributions.
Abstract: Matching is a powerful statistical tool in design and analysis. Conventional two-group, or bipartite, matching has been widely used in practice. However, its utility is limited to simpler designs. In contrast, nonbipartite matching is not limited to the two-group case, handling multiparty matching situations. It can be used to find the set of matches that minimize the sum of distances based on a given distance matrix. It brings greater flexibility to the matching design, such as multigroup comparisons. Thanks to improvements in computing power and freely available algorithms to solve nonbipartite problems, the cost in terms of computation time and complexity is low. This article reviews the optimal nonbipartite matching algorithm and its statistical applications, including observational studies with complex designs and an exact distribution-free test comparing two multivariate distributions. We also introduce an R package that performs optimal nonbipartite matching. We present an easily accessible web application to make nonbipartite matching freely available to general researchers.
17 Oct 2011
TL;DR: In this paper, the authors consider the many-to-one matching market where peer effects are derived from an underlying social network and show that stable matchings always exist and characterize the set of stable matching in terms of social welfare.
Abstract: Many-to-one matching markets exist in numerous different forms, such as college admissions, matching medical interns to hospitals for residencies, assigning housing to college students, and the classic firms and workers market. In all these markets, externalities such as complementarities and peer effects severely complicate the preference ordering of each agent. Further, research has shown that externalities lead to serious problems for market stability and for developing efficient algorithms to find stable matchings. In this paper we make the observation that peer effects are often the result of underlying social connections, and we explore a formulation of the many-to-one matching market where peer effects are derived from an underlying social network. The key feature of our model is that it captures peer effects and complementarities using utility functions, rather than traditional preference ordering. With this model and considering a weaker notion of stability, namely twosided exchange stability, we prove that stable matchings always exist and characterize the set of stable matchings in terms of social welfare. To characterize the efficiency of matching markets with externalities, we provide general bounds on how far the welfare of the worst-case stable matching can be from the welfare of the optimal matching, and find that the structure of the social network (e.g. how well clustered the network is) plays a large role.
TL;DR: In this article, the authors apply matching theory to examine entrepreneurs' intentions to add new ties to their personal networks, and propose that task complementarity can be used to complement matching theory.
Abstract: This study advances understanding of network dynamics by applying matching theory to examine entrepreneurs' intentions to add new ties to their personal networks. I propose that task complementarit...
27 Jun 2011
TL;DR: A simple method to control for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI) data using support vector machine classification (SVM) or voxel-based morphometry (VBM) is proposed.
Abstract: In recent research, many univariate and multivariate approaches have been proposed to improve automatic classification of various dementia syndromes using imaging data. Some of these methods do not provide the possibility to integrate possible confounding variables like age into the statistical evaluation. A similar problem sometimes exists in clinical studies, as it is not always possible to match different clinical groups to each other in all confounding variables, like for example, early-onset (age<65 years) and late-onset (age≥65) patients with Alzheimer's disease (AD). Here, we propose a simple method to control for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI) data using support vector machine classification (SVM) or voxel-based morphometry (VBM). We compare SVM results for the classification of 80 AD patients and 79 healthy control subjects based on MRI data with and without prior age correction. Additionally, we compare VBM results for the comparison of three different groups of AD patients differing in age with the same group of control subjects obtained without including age as covariate, with age as covariate or with prior age correction using the proposed method. SVM classification using the proposed method resulted in higher between-group classification accuracy compared to uncorrected data. Further, applying the proposed age correction substantially improved univariate detection of disease-related grey matter atrophy using VBM in AD patients differing in age from control subjects. The results suggest that the approach proposed in this work is generally suited to control for confounding variables such as age in SVM or VBM analyses. Accordingly, the approach might improve and extend the application of these methods in clinical neurosciences.
15 Jun 2011
TL;DR: The concept of matching social network and corporate hierarchy in organizations with stable corporate structure is presented to confirm whether social position of an employee calculated on the basis of the social network differs significantly from the formal employee role in the company.
Abstract: The following paper presents the concept of matching social network and corporate hierarchy in organizations with stable corporate structure. The idea allows to confirm whether social position of an employee calculated on the basis of the social network differs significantly from the formal employee role in the company. The results of such analysis may lead to possible company management improvement enabling to gain a competitive edge. In order to perform this task the authors have made experiments with the use of two real-life datasets: Enron and mid-sized manufacturing companies showing which social network metrics may be suitable to match organizational structure and social network with good results.
TL;DR: The various approaches in multivariable modelling of healthcare costs data are described and their respective criticisms are synthesized to synthesize the respective criticisms as proposed in the literature.
Abstract: Objectives. This article aims to describe the various approaches in multivariable modelling of healthcare costs data and to synthesize the respective criticisms as proposed in the literature. Methods. We present regression methods suitable for the analysis of healthcare costs and then apply them to an experimental setting in cardiovascular treatment (COSTAMI study) and an observational setting in diabetes hospital care. Results. We show how methods can produce different results depending on the degree of matching between the underlying assumptions of each method and the specific characteristics of the healthcare problem. Conclusions. The matching of healthcare cost models to the analytic objectives and characteristics of the data available to a study requires caution. The study results and interpretation can be heavily dependent on the choice of model with a real risk of spurious results and conclusions.
21 Aug 2011
TL;DR: A methodology for extracting mobility profiles of individuals from raw digital traces (in particular, GPS traces), and study criteria to match individuals based on profiles, and a matching criterion that satisfies various basic constraints obtained from the background knowledge of the application domain are introduced.
Abstract: In this paper we introduce a methodology for extracting mobility profiles of individuals from raw digital traces (in particular, GPS traces), and study criteria to match individuals based on profiles. We instantiate the profile matching problem to a specific application context, namely proactive car pooling services, and therefore develop a matching criterion that satisfies various basic constraints obtained from the background knowledge of the application domain. In order to evaluate the impact and robustness of the methods introduced, two experiments are reported, which were performed on a massive dataset containing GPS traces of private cars: (i) the impact of the car pooling application based on profile matching is measured, in terms of percentage shareable traffic; (ii) the approach is adapted to coarser-grained mobility data sources that are nowadays commonly available from telecom operators. In addition the ensuing loss in precision and coverage of profile matches is measured.
TL;DR: This article reviews some of this recent literature on mechanism design approach to student assignment, highlighting how issues from the field motivated theoretical developments and emphasizing how the dialogue may be a road map for other areas of applied mechanism design.
Abstract: The mechanism design approach to student assignment involves the theoretical, empirical, and experimental study of systems used to allocate students into schools around the world. Recent practical experience designing systems for student assignment has raised new theoretical questions for the theory of matching and assignment. This article reviews some of this recent literature, highlighting how issues from the field motivated theoretical developments and emphasizing how the dialogue may be a road map for other areas of applied mechanism design. Finally, it concludes with some open questions.
01 Sep 2011
TL;DR: This paper motivates and explains the Semi-Global Matching technique, shows current developments as well as examples from various applications, and is well suited for solving practical problems.
Abstract: Since its original publication, the Semi-Global Matching (SGM) technique has been re-implemented by many researchers and companies The method offers a very good trade off between runtime and accuracy, especially at object borders and fine structures It is also robust against radiometric differences and not sensitive to the choice of parameters Therefore, it is well suited for solving practical problems The applications reach from remote sensing, like deriving digital surface models from aerial and satellite images, to robotics and driver assistance systems This paper motivates and explains the method, shows current developments as well as examples from various applications