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 Models: An Overview

Abstract: Given the general class of spatial interaction processes outlined in Chapter 1, we are now ready to develop the specific class of behavioral models which form the central focus of this book — namely gravity models of spatial interaction behavior. To do so, we begin by recalling from the discussion following the Poisson Characterization Theorem in Chapter 1 that each independent interaction process, P = {Pc:c ∈ C}, is completely characterized by its associated mean interaction frequencies, Ec(N ij ), ij ∈ I x J, for each separation configuration, c ∈ C. Hence each explicit model of mean interaction frequencies yields a complete specification of probabilistic interaction behavior in this context. With this observation in mind, recall from the Introduction that gravity models are precisely of this type. In particular, if the ‘interaction levels’, T ij , in expressions (2) through (4) in the Introduction are now interpreted as mean interaction frequencies for the separation configuration defined by distances, d ij then each of these expressions is seen to constitute an explicit (finite parameter) model of mean interaction frequencies. More generally, even for spatial interaction processes in which the axioms of frequency independence and/or locational independence are not appropriate, gravity models may still be interpreted as representations of average interaction behavior within such processes.

Nonresponse and Urban Travel Models

Abstract: Considerable attention has been paid to the presence of nonresponse in large-scale travel surveys on the basis of which urban travel demand models are developed. It has been shown that the effect of nonresponse can be reduced by careful model building, with categorical trip generation models as an example. The same philosophy is extended to logit mode split models and exponential gravity models to show that the usual levels of nonresponse that one encounters in urban travel surveys have virtually no adverse effects on the parameter estimates of these models if the model has been specified correctly. Some simulation results are also presented to show the behavior of logit and exponential gravity model parameter estimates under conditions on nonresponse.

Multiworker household travel demand. chapter 4, volume i demographic special reports - in: 1990 npts special reports

Abstract: The purpose of this study is to examine the travel behavior and related characteristics of multiworker households (MWHs) (defined as households with at least two workers) and how they contribute to the ever-increasing demand for transportation services. On average they have incomes which exceed the national household average and often have multiple automobiles and as households they generate a considerable number of trips. The virtual dearth of previous studies of MWHs makes an overview of their characteristics and their travel behavior necessary. This study reveals that the number of MWHs has continued to grow, as has their use of highways; they are found in disproportionate numbers in low density urban areas distant from public transportation. They also have new vehicles, and drive each vehicle more miles than other households. As households, MWHs travel more than do other households. However, an individual worker's ability and desire to travel is constrained by time factors, among others, and transportation use by MWHs, when calculated on a per worker basis, is relatively low.

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.