A. S. Hedayat
Other affiliations: University of California, Berkeley, University of Illinois at Urbana–Champaign, University of Guelph ...read more
Bio: A. S. Hedayat is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Orthogonal array & Optimal design. The author has an hindex of 34, co-authored 147 publications receiving 12777 citations. Previous affiliations of A. S. Hedayat include University of California, Berkeley & University of Illinois at Urbana–Champaign.
Papers published on a yearly basis
TL;DR: In this article, the authors review the literature and present methodologies in terms of coverage probability for all of the aforementioned measurements when the target values are fixed and when the error structure is homogenous or heterogeneous.
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...
TL;DR: Hadamard matrices have been widely studied in the literature and many of their applications can be found in this paper, e.g., incomplete block designs, Youden designs, orthogonal $F$-square designs, optimal saturated resolution III (SRSIII), optimal weighing designs, maximal sets of pairwise independent random variables with uniform measure, error correcting and detecting codes, Walsh functions, and other mathematical and statistical objects.
Abstract: An $n \times n$ matrix $H$ with all its entries $+1$ and $-1$ is Hadamard if $HH' = nI$. It is well known that $n$ must be 1, 2 or a multiple of 4 for such a matrix to exist, but is not known whether Hadamard matrices exist for every $n$ which is a multiple of 4. The smallest order for which a Hadamard matrix has not been constructed is (as of 1977) 268. Research in the area of Hadamard matrices and their applications has steadily and rapidly grown, especially during the last three decades. These matrices can be transformed to produce incomplete block designs, $t$-designs, Youden designs, orthogonal $F$-square designs, optimal saturated resolution III designs, optimal weighing designs, maximal sets of pairwise independent random variables with uniform measure, error correcting and detecting codes, Walsh functions, and other mathematical and statistical objects. In this paper we survey the existence of Hadamard matrices and many of their applications.
TL;DR: In this article, the authors present a list of repeated measurements designs for those cases where a subject cannot participate in all tests as in many pharmacological studies, and provide an extensive list of references on repeated measurements which it is hoped, will be useful to those who want to do further research.
Abstract: : Repeated measurements designs are concerned with scientific experiments in which subjects (experimental units) are repeatedly exposed to a sequence of different or identical tests (treatments). These designs have application in many branches of scientific inquiry such as: Biology, education, food science, marketing, environmental engineering, medicine and pharmacology. The objectives in the paper are threefold: (1) To construct some families of repeated measurements designs which researchers have been seeking. These designs are useful for those cases where a subject cannot participate in all tests as in many pharmacological studies; (2) to provide an extensive list of references on repeated measurements designs which it is hoped, will be useful to those who want to do further research in this area; (3) to state some unsolved problems which have an immediate application. (Author)
01 Jan 1991
TL;DR: The Horvitz-Thompson Estimator as mentioned in this paper has been used extensively for small area estimation, including in the context of finite population sampling, and is a data gathering tool for sensitive characteristics.
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.
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON
TL;DR: In this article, the sensitivity of maximum likelihood (ML), generalized least squares (GLS), and asymptotic distribution-free (ADF)-based fit indices to model misspecification, under conditions that varied sample size and distribution.
Abstract: This study evaluated the sensitivity of maximum likelihood (ML)-, generalized least squares (GLS)-, and asymptotic distribution-free (ADF)-based fit indices to model misspecification, under conditions that varied sample size and distribution. The effect of violating assumptions of asymptotic robustness theory also was examined. Standardized root-mean-square residual (SRMR) was the most sensitive index to models with misspecified factor covariance(s), and Tucker-Lewis Index (1973; TLI), Bollen's fit index (1989; BL89), relative noncentrality index (RNI), comparative fit index (CFI), and the MLand GLS-based gamma hat, McDonald's centrality index (1989; Me), and root-mean-square error of approximation (RMSEA) were the most sensitive indices to models with misspecified factor loadings. With ML and GLS methods, we recommend the use of SRMR, supplemented by TLI, BL89, RNI, CFI, gamma hat, Me, or RMSEA (TLI, Me, and RMSEA are less preferable at small sample sizes). With the ADF method, we recommend the use of SRMR, supplemented by TLI, BL89, RNI, or CFI. Finally, most of the ML-based fit indices outperformed those obtained from GLS and ADF and are preferable for evaluating model fit.
TL;DR: For example, this paper found that females value cues to resource acquisition in potential mates more highly than males, while males valued earning capacity, ambition, industriousness, youth, physical attractiveness, and chastity.
Abstract: Contemporary mate preferences can provide important clues to human reproductive history. Little is known about which characteristics people value in potential mates. Five predictions were made about sex differences in human mate preferences based on evolutionary conceptions of parental investment, sexual selection, human reproductive capacity, and sexual asymmetries regarding certainty of paternity versus maternity. The predictions centered on how each sex valued earning capacity, ambition— industriousness, youth, physical attractiveness, and chastity. Predictions were tested in data from 37 samples drawn from 33 countries located on six continents and five islands (total N = 10,047). For 27 countries, demographic data on actual age at marriage provided a validity check on questionnaire data. Females were found to value cues to resource acquisition in potential mates more highly than males. Characteristics signaling reproductive capacity were valued more by males than by females. These sex differences may reflect different evolutionary selection pressures on human males and females; they provide powerful cross-cultural evidence of current sex differences in reproductive strategies. Discussion focuses on proximate mechanisms underlying mate preferences, consequences for human intrasexual competition, and the limitations of this study.
01 Jan 1980
TL;DR: The statistical similarities among mediation, confounding, and suppression are described and methods to determine the confidence intervals for confounding and suppression effects are proposed based on methods developed for mediated effects.
Abstract: This paper describes the statistical similarities among mediation, confounding, and suppression. Each is quantified by measuring the change in the relationship between an independent and a dependent variable after adding a third variable to the analysis. Mediation and confounding are identical statistically and can be distinguished only on conceptual grounds. Methods to determine the confidence intervals for confounding and suppression effects are proposed based on methods developed for mediated effects. Although the statistical estimation of effects and standard errors is the same, there are important conceptual differences among the three types of effects.