Download or read book Pooling Restricted Maximum Likelihood Estimates from Data Subsets Under the Animal Model written by Robert Peter Yerex and published by . This book was released on 1988 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Pooling Maximum Likelihood Estimates of Variance Components Obtained from Subsets of Unbalanced Data written by James Scott Babb and published by . This book was released on 1986 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dissertation Abstracts International written by and published by . This book was released on 1989 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings written by and published by . This book was released on 1992 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Maximum Likelihood Estimation for Sample Surveys written by Raymond L. Chambers and published by CRC Press. This book was released on 2012-05-02 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to

Download or read book Restricted Maximum Likelihood Estimation of Covariances in Sparse Linear Models written by Arnold Neumaier and published by . This book was released on 1995 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book SAS for Mixed Models written by Walter W. Stroup and published by SAS Institute. This book was released on 2018-12-12 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.

Download or read book A Pseudo Restricted Maximum Likelihood Estimator Under Multivariate Simple Tree Order Restriction and an Algorithm written by Huruy Debessay Asfha and published by . This book was released on 2021 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: The minimum distance projection of a given matrix $X \in R^{pxq}$ onto the order restricted cone in an appropriately defined inner product system, $\pi(X|C_{pxq}),$ plays an important role in order restricted statistical inference since in many cases the restricted maximum likelihood estimator (RMLE) for a parameter matrix under an order restriction is the projection of the maximum likelihood estimator (MLE) without any restrictions onto the order restricted cone. The RMLE plays an important part in the maximum likelihood ratio tests. The computation for $\pi(X|_{pxq}),$ however is currently a great challenge to researchers. It is known that the order relation $\preceq$ in $R^p$ is a multivariate order relation if and only if it is generated from a closed convex cone $C \in R^p$, called an order generating cone. The collection of all matrices $\mu = (\mu_1,...,\mu_q) \in R^{pxq}$ whose columns satisfy the multivariate order restriction $\mu i \preceq \mu i$ for all $(i, j)$ in a specified set $H \subset$ {1,...,q} x {1,...,q} is a closed convex cone $C_{pxq}$ in $R^{pxq}$ called an order restricted cone. For $C_{pxq}$ created by multivariate simpletree order restriction and a given matrix $X \in R^{pxq}$, in this dissertation, a closed convex subset $D(X)_{pxq} \subset C_{pxq}$ is defined. The projection of X onto this subset, $\pi(X|D(X0_{pxq})$, is studied. In addition, an algorithm for computing $\pi(X|D(X)_{pxq})$ is proposed and proved. The proposed algorithm for $\pi(X|D(X)_{pxq})$ only depends on projections of vectors onto the order generating cone. Thus, it converts the relatively difficult matrix projection problem to a much easier vector projection problems. It is also revealed that when q = 2, $\pi(X|D(X)_{pxq}) = \pi(X|C_{pxq})$ and if $X \in C_{pxq}$, $\pi(X|D(X)_{pxq}) = \pi(X|C_{pxq})$. With all these good properties we could treat the projection onto $D(X)_{pxq}$ as the approximation of the projection onto $C_{pxq}.

Download or read book Maximum Likelihood Estimation of Parameters with Constraints in Normal and Multinomial Distributions written by HUITIAN. XUE and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Maximum Likelihood Estimation of Parameters With Constraints in Normal and Multinomial Distributions" by Huitian, Xue, 薛惠天, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Motivated by problems in medicine, biology, engineering and economics, con- strained parameter problems arise in a wide variety of applications. Among them the application to the dose-response of a certain drug in development has attracted much interest. To investigate such a relationship, we often need to conduct a dose- response experiment with multiple groups associated with multiple dose levels of the drug. The dose-response relationship can be modeled by a shape-restricted normal regression. We develop an iterative two-step ascent algorithm to estimate normal means and variances subject to simultaneous constraints. Each iteration consists of two parts: an expectation{maximization (EM) algorithm that is utilized in Step 1 to compute the maximum likelihood estimates (MLEs) of the restricted means when variances are given, and a newly developed restricted De Pierro algorithm that is used in Step 2 to find the MLEs of the restricted variances when means are given. These constraints include the simple order, tree order, umbrella order, and so on. A bootstrap approach is provided to calculate standard errors of the restricted MLEs. Applications to the analysis of two real datasets on radioim-munological assay of cortisol and bioassay of peptides are presented to illustrate the proposed methods. Liu (2000) discussed the maximum likelihood estimation and Bayesian estimation in a multinomial model with simplex constraints by formulating this constrained parameter problem into an unconstrained parameter problem in the framework of missing data. To utilize the EM and data augmentation (DA) algorithms, he introduced latent variables {Zil;Yil} (to be defined later). However, the proposed DA algorithm in his paper did not provide the necessary individual conditional distributions of Yil given (the observed data and) the updated parameter estimates. Indeed, the EM algorithm developed in his paper is based on the assumption that{ Yil} are fixed given values. Fortunately, the EM algorithm is invariant under any choice of the value of Yil, so the final result is always correct. We have derived the aforesaid conditional distributions and hence provide a valid DA algorithm. A real data set is used for illustration. DOI: 10.5353/th_b4785001 Subjects: Estimation theory Parameter estimation

Download or read book Restricted Maximum Likelihood Estimation with Restricted Parameter Spaces written by James Arthur Calvin and published by . This book was released on 1992 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Restricted Maximum Likelihood Estimation of Variance Components written by Terrance Patrick Callanan and published by . This book was released on 1985 with total page 774 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Restricted Maximum Likelihood Estimation for Two Variance Components written by Justus Seely and published by . This book was released on 1972 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Comparison of Restricted Maximum Likelihood and Method of Moments Variance Estimation for Small sample Split plot Experiments written by Xuan Xu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers may choose to perform an experiment using a split-plot design over more simple designs such as a completely randomized design or a randomized complete block design in order to conserve scarce resources. A split-plot becomes attractive when some treatment factors are more costly to apply to the experimental units or when it is difficult to change one factor from level to level. In such a case it may be more efficient to apply these costly treatments to a small set of larger experimental units (i.e. whole plots) and then apply the less costly treatments to more numerous smaller experimental units (i.e. subplots) nested within the larger ones. Because the subplot and whole-plot experimental units each have a corresponding variance component, the analysis of a split-plot study is more complicated. Making the split-plot analysis even more challenging, cost considerations may also lead to relatively small sample sizes for the whole-plot treatments. An unintended consequence is that some variance components in the split-plot design's model may be poorly estimated which in turn may have an unanticipated effect on the type I error rates for tests of the fixed effects. As a motivating example, alfalfa yield data from a field study with a split-plot design with four randomized complete blocks at the whole-plot level serves as the basis for a simulation study to estimate the type I error rates of three fixed effects (whole-plot main effects, subplot main effects and whole-plot by subplot interaction). Several other scenarios where the number of blocks and the relative magnitudes of the variance components are varied are also explored. For each scenario, 10,000 data sets were randomly generated assuming normally distributed errors. Two linear mixed models were fit to each data set using the MIXED procedure in SAS; one method estimates the variance components via restricted maximum likelihood (REML) and the other by the method of moments (MoM) based on the type III sums of squares. The REML models yielded inconsistent type I error rates for some tests of fixed effects compared to the MoM models but improved as the number of blocks increased. MoM models tended to hold their nominal type I error rates to within expected Monte Carlo error.

Download or read book Modeling Ordered Choices written by William H. Greene and published by Cambridge University Press. This book was released on 2010-04-08 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is increasingly common for analysts to seek out the opinions of individuals and organizations using attitudinal scales such as degree of satisfaction or importance attached to an issue. Examples include levels of obesity, seriousness of a health condition, attitudes towards service levels, opinions on products, voting intentions, and the degree of clarity of contracts. Ordered choice models provide a relevant methodology for capturing the sources of influence that explain the choice made amongst a set of ordered alternatives. The methods have evolved to a level of sophistication that can allow for heterogeneity in the threshold parameters, in the explanatory variables (through random parameters), and in the decomposition of the residual variance. This book brings together contributions in ordered choice modeling from a number of disciplines, synthesizing developments over the last fifty years, and suggests useful extensions to account for the wide range of sources of influence on choice.

Download or read book Journal of Animal Science written by and published by . This book was released on 1985 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Variance Components written by Shayle R. Searle and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. ". . .Variance Components is an excellent book. It is organized and well written, and provides many references to a variety of topics. I recommend it to anyone with interest in linear models." —Journal of the American Statistical Association "This book provides a broad coverage of methods for estimating variance components which appeal to students and research workers . . . The authors make an outstanding contribution to teaching and research in the field of variance component estimation." —Mathematical Reviews "The authors have done an excellent job in collecting materials on a broad range of topics. Readers will indeed gain from using this book . . . I must say that the authors have done a commendable job in their scholarly presentation." —Technometrics This book focuses on summarizing the variability of statistical data known as the analysis of variance table. Penned in a readable style, it provides an up-to-date treatment of research in the area. The book begins with the history of analysis of variance and continues with discussions of balanced data, analysis of variance for unbalanced data, predictions of random variables, hierarchical models and Bayesian estimation, binary and discrete data, and the dispersion mean model.

Download or read book Doing Meta Analysis with R written by Mathias Harrer and published by CRC Press. This book was released on 2021-09-15 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book