Ashish Sen

Bio: Ashish Sen is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Travel behavior & Covariance. The author has an hindex of 15, co-authored 40 publications receiving 2080 citations.

Gravity model applications in health planning: analysis of an urban hospital market*

Abstract: . In this paper a gravity model is formulated to model a hospital patient flow system. Both spatial and nonspatial measures of separation are used to estimate the model. Travel time was found to provide improved fits over linear distance in modeling patient flow behavior. Transformations of travel time (square root and log) were found to improve model performance further. Model diagnostics suggested additional spatial separation measurw, separation measure parameters demonstrated stability over time. The model is used to forecast the effects of health care financing reform and hospital closure on patient flows in an urban hospital market. Universal health coverage was found to dramatically improve access to hospital services for lower income patient populations. Hospitals at risk for closure were forecast to receive increased patient flows. Medical school hospitals are expected to lose patients after implementation of health reform, with important implications for graduate medical education. Hospital closures were shown to have negative effects on access to services in poor neighborhoods, but relatively little effect on access in the system as a whole.

Maximum likelihood estimation of gravity model parameters

Abstract: . It is shown that, under some very mild conditions, maximum likelihood estimates of gravity model parameters exist and are unique (up to a scale transformation for some parameters). An algorithm for finding such estimates is also proposed.

Quality of information given by advanced traveler information systems

Abstract: A number of studies have evaluated the services provided by Advanced Traveler Information Systems [ATIS] under the assumption that information supplied to drivers would be, in some sense, perfect. However, lack of sufficiently useful data and system design constraints can lead to information that is less than useful to the ATIS user. This paper examines the effects of such imperfection through a simulation-based model that was applied over a part of a large metropolitan area. The model has four basic components: 1. (i) an ATIS structure (that specifies the information-gathering, processing and disseminating aspects of the system) 2. (ii) traveler behavior 3. (iii) network characteristics 4. (iv) vehicle movement logic. Using a ‘yoked driver’ concept, a number of different route guidance strategies are examined. The results indicate that some strategies that would appear to be desirable are not so. Conversely, under high-congestion situations, strategies can be constructed that come close to ‘rectifying’ completely the effects of information imperfection. Overall the paper reiterates the potential of ATIS if information-giving strategies are designed carefully.

Estimating gravity model parameters: a simplified approach based on the odds ratio

Abstract: Two computationally simple methods for calibrating the gravity model are presented in this paper. The use of each is demonstrated on several origin-destination trip tables, and the results compared with those obtained from the UTP system gravity model calibration procedure. Our methods, based on the odds ratio, perform at least as well as that of UTP system by several standard criteria.

SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials

Abstract: High quality protocols facilitate proper conduct, reporting, and external review of clinical trials. However, the completeness of trial protocols is often inadequate. To help improve the content and quality of protocols, an international group of stakeholders developed the SPIRIT 2013 Statement (Standard Protocol Items: Recommendations for Interventional Trials). The SPIRIT Statement provides guidance in the form of a checklist of recommended items to include in a clinical trial protocol. This SPIRIT 2013 Explanation and Elaboration paper provides important information to promote full understanding of the checklist recommendations. For each checklist item, we provide a rationale and detailed description; a model example from an actual protocol; and relevant references supporting its importance. We strongly recommend that this explanatory paper be used in conjunction with the SPIRIT Statement. A website of resources is also available (www.spirit-statement.org). The SPIRIT 2013 Explanation and Elaboration paper, together with the Statement, should help with the drafting of trial protocols. Complete documentation of key trial elements can facilitate transparency and protocol review for the benefit of all stakeholders.

Circular binary segmentation for the analysis of array-based DNA copy number data.

Abstract: DNA sequence copy number is the number of copies of DNA at a region of a genome. Cancer progression often involves alterations in DNA copy number. Newly developed microarray technologies enable simultaneous measurement of copy number at thousands of sites in a genome. We have developed a modification of binary segmentation, which we call circular binary segmentation, to translate noisy intensity measurements into regions of equal copy number. The method is evaluated by simulation and is demonstrated on cell line data with known copy number alterations and on a breast cancer cell line data set.

Optimal Detection of Changepoints With a Linear Computational Cost

Abstract: In this article, we consider the problem of detecting multiple changepoints in large datasets. Our focus is on applications where the number of changepoints will increase as we collect more data: for example, in genetics as we analyze larger regions of the genome, or in finance as we observe time series over longer periods. We consider the common approach of detecting changepoints through minimizing a cost function over possible numbers and locations of changepoints. This includes several established procedures for detecting changing points, such as penalized likelihood and minimum description length. We introduce a new method for finding the minimum of such cost functions and hence the optimal number and location of changepoints that has a computational cost, which, under mild conditions, is linear in the number of observations. This compares favorably with existing methods for the same problem whose computational cost can be quadratic or even cubic. In simulation studies, we show that our new method can...

Penalized Regressions: The Bridge versus the Lasso

Abstract: Bridge regression, a special family of penalized regressions of a penalty function Σ|βj|γ with γ ≤ 1, considered. A general approach to solve for the bridge estimator is developed. A new algorithm for the lasso (γ = 1) is obtained by studying the structure of the bridge estimators. The shrinkage parameter γ and the tuning parameter λ are selected via generalized cross-validation (GCV). Comparison between the bridge model (γ ≤ 1) and several other shrinkage models, namely the ordinary least squares regression (λ = 0), the lasso (γ = 1) and ridge regression (γ = 2), is made through a simulation study. It is shown that the bridge regression performs well compared to the lasso and ridge regression. These methods are demonstrated through an analysis of a prostate cancer data. Some computational advantages and limitations are discussed.

changepoint: An R Package for Changepoint Analysis

Abstract: One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence. The changepoint package has been developed to provide users with a choice of multiple changepoint search methods to use in conjunction with a given changepoint method and in particular provides an implementation of the recently proposed PELT algorithm. This article describes the search methods which are implemented in the package as well as some of the available test statistics whilst highlighting their application with simulated and practical examples. Particular emphasis is placed on the PELT algorithm and how results differ from the binary segmentation approach.