Bikas K. Sinha

Bio: Bikas K. Sinha is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Optimal design & Estimator. The author has an hindex of 14, co-authored 133 publications receiving 1634 citations. Previous affiliations of Bikas K. Sinha include University of Illinois at Chicago.

Statistical methods in assessing agreement: Models, issues, and tools

Abstract: Measurements of agreement are needed to assess the acceptability of a new or generic process, methodology, and formulation in areas of laboratory performance, instrument or assay validation, method comparisons, statistical process control, goodness of fit, and individual bioequivalence. In all of these areas, one needs measurements that capture a large proportion of data that are within a meaningful boundary from target values. Target values can be considered random (measured with error) or fixed (known), depending on the situation. Various meaningful measures to cope with such diverse and complex situations have become available only in the last decade. These measures often assume that the target values are random. This article reviews the literature and presents methodologies in terms of “coverage probability.” In addition, analytical expressions are introduced for all of the aforementioned measurements when the target values are fixed and when the error structure is homogenous or heterogeneous (proport...

Theory of Optimal Designs

Abstract: 1. Optimality Criteria In Design of Experiments.- 1. General Objectives.- 2. The Linear Model Set-up.- 3. Choice of Optimality Criteria.- References.- 2. Block Designs: General Optimality.- 1. Introduction.- 2. Universal Optimality of the BBDs.- 3. Optimality of Some Classes of Asymmetrical Designs w.r.t the Generalized Criteria.- References.- 3. Block Designs: Specific Optimality.- 1. Introduction.- 2. E-optimal Designs.- 3. Efficiency Factor and A-optimal Designs.- 4. MV-optimal Designs.- 5. D-optimal Designs.- 6. Regular Graph Designs and John-Mitchell Conjecture.- 7. Optimal Designs with Unequal Block Sizes.- References.- 4 Row-Column Designs.- 1. Introduction.- 2. Universal Optimality of the Regular GYDs.- 3. Nonregular GYDs: Specific Optimality Results.- 4. Optimality of Other Row-Column Designs.- References.- 5. Mixed Effects Models.- 1. Introduction.- 2. Optimality Aspects of Block Designs Under a Mixed Effects Model.- 3. Optimality of GYDs Under a Mixed Effects Model.- 4. Concluding Remarks.- References.- 6. Repeated Measurements Designs.- 1. Introduction.- 2. The Linear Model(s), Definitions and Notations.- 3. Universal Optimality of Strongly Balanced Uniform RMDs.- 4. Universal Optimality of Nearly Strongly Balanced Uniform RMDs.- 5. Universal Optimality of Balanced Uniform RMDs.- 6. Concluding Remarks.- References.- 7. Optimal Designs For Some Special Cases.- 1. Introduction.- 2. Models with Correlated Observations.- 3. Models with Covariates.- 4. Designs for Comparing Treatments vs. Control.- References.- 8. Weighing Designs.- 1. Introduction.- 2. A Study of Chemical Balance Weighing Designs.- 3. A Study of Spring Balance Weighing Designs.- 4. Optimal Estimation of Total Weight.- 5. Miscellaneous Topics in Weighing Designs.- References.- Author Index.

Design and inference in finite population sampling

Abstract: A Unified Setup for Probability Sampling. Inference in Finite Population Sampling. The Horvitz--Thompson Estimator. Simple Random and Allied Sampling Designs. Uses of Auxiliary Size Measures in Survey Sampling: Strategies Based on Probability Proportional to Size Schemes of Sampling. Uses of Auxiliary Size Measures in Survey Sampling: Ratio and Regression Methods of Estimation. Cluster Sampling Designs. Systematic Sampling Designs. Stratified Sampling Designs. Superpopulation Approach to Inference in Finite Population Sampling. Randomized Response: A Data--Gathering Tool for Sensitive Characteristics. Special Topics: Small Area Estimation, Nonresponse Problems, and Resampling Techniques. Author Index. Subject Index.

Design Issues for Generalized Linear Models: A Review

Abstract: Generalized linear models (GLMs) have been used quite effectively in the modeling of a mean response under nonstandard conditions, where discrete as well as continuous data distributions can be accommodated. The choice of design for a GLM is a very important task in the development and building of an adequate model. However, one major problem that handicaps the construction of a GLM design is its dependence on the unknown parameters of the fitted model. Several approaches have been proposed in the past 25 years to solve this problem. These approaches, however, have provided only partial solutions that apply in only some special cases, and the problem, in general, remains largely unresolved. The purpose of this article is to focus attention on the aforementioned dependence problem. We provide a survey of various existing techniques dealing with the dependence problem. This survey includes discussions concerning locally optimal designs, sequential designs, Bayesian designs and the quantile dispersion graph approach for comparing designs for GLMs.

Topics in Optimal Design

Abstract: Scope of the Monograph * Optimal Regression Designs in Symmetric Domains * Optimal Regression Designs in Asymmetric Factor Domains * Optimal Regression Designs for Covariates' Models with Structured Intercept Parameter * Stochastic Distance Optimality * Designs in the Presence of Trends * Additional Selected Topics

Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood

Abstract: Restricted maximum likelihood (REML) is now well established as a method for estimating the parameters of the general Gaussian linear model with a structured covariance matrix, in particular for mixed linear models. Conventionally, estimates of precision and inference for fixed effects are based on their asymptotic distribution, which is known to be inadequate for some small-sample problems. In this paper, we present a scaled Wald statistic, together with an F approximation to its sampling distribution, that is shown to perform well in a range of small sample settings. The statistic uses an adjusted estimator of the covariance matrix that has reduced small sample bias. This approach has the advantage that it reproduces both the statistics and F distributions in those settings where the latter is exact, namely for Hotelling T2 type statistics and for analysis of variance F-ratios. The performance of the modified statistics is assessed through simulation studies of four different REML analyses and the methods are illustrated using three examples.

Design and Analysis of Cross-Over Trials

Abstract: This chapter provides an overview of recent developments in the design and analysis of cross-over trials. We first consider the analysis of the trial that compares two treatments, A and B, over two periods and where the subjects are randomized to the treatment sequences AB and BA. We make the distinction between fixed and random effects models and show how these models can easily be fitted using modern software. Issues with fitting and testing for a difference in carry-over effects are described and the use of baseline measurements is discussed. Simple methods for testing for a treatment difference when the data are binary are also described. Various designs with two or more treatments but with three or four periods are then described and compared. These include the balanced and partially balanced designs for three or more treatments and designs for factorial treatment combinations. Also described are nearly balanced and nearly strongly balanced designs. Random subject-effects models for the designs with two or more treatments are described and methods for analysing non-normal data are also given. The chapter concludes with a description of the use of cross-over designs in the testing of bioequivalence.

Continuous Multivariate Distributions

Abstract: In this article, we present a concise review of developments on various continuous multivariate distributions. We first present some basic definitions and notations. Then, we present several important continuous multivariate distributions and list their significant properties and characteristics. Keywords: generating function; moments; conditional distribution; truncated distribution; regression; bivariate normal; multivariate normal; multivariate exponential; multivariate gamma; dirichlet; inverted dirichlet; liouville; multivariate logistic; multivariate pareto; multivariate extreme value; multivariate t; wishart translated systems; multivariate exponential families

Response Surface Methodology.

Abstract: The purpose of this article is to provide a survey of the various stages in the development of response surface methodology RSM. The coverage of these stages is organized in three parts that describe the evolution of RSM since its introduction in the early 1950s. Part I covers the period, 1951-1975, during which the so-called classical RSM was developed. This includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions. Part II, which covers the period, 1976-1999, discusses more recent modeling techniques in RSM, in addition to a coverage of Taguchi's robust parameter design and its response surface alternative approach. Part III provides a coverage of further extensions and research directions in modern RSM. This includes discussions concerning response surface models with random effects, generalized linear models, and graphical techniques for comparing response surface designs. Copyright © 2010 John Wiley & Sons, Inc.