Richly Parameterized Linear Models: Additive, Time Series, by James S. Hodges PDF

By James S. Hodges

ISBN-10: 143986683X

ISBN-13: 9781439866832

ISBN-10: 1439866848

ISBN-13: 9781439866849

''This ebook covers a variety of statistical versions, together with hierarchical, hierarchical generalized linear, linear combined, dynamic linear, smoothing, spatial, and longitudinal. It offers a framework for expressing those richly parameterized versions jointly in addition to instruments for exploring and analyzing the result of becoming the versions to facts. It extends the traditional conception of linear types and illustrates the Read more...

Show description

Read or Download Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects PDF

Similar probability & statistics books

Read e-book online Maths & Stats Mathematical Statistics PDF

This graduate textbook covers subject matters in statistical concept crucial for graduate scholars getting ready for paintings on a Ph. D. measure in statistics. the 1st bankruptcy presents a short assessment of suggestions and ends up in measure-theoretic likelihood concept which are valuable in information. the second one bankruptcy introduces a few basic ideas in statistical determination thought and inference.

Download e-book for iPad: A Whistle-Stop Tour of Statistics by Brian S. Everitt

Entrance disguise; commitment; desk of content material; Preface; 1. a few fundamentals and Describing information; 2. chance; three. Estimation; four. Inference; five. research of Variance types; 6. Linear Regression versions; 7. Logistic Regression and the Generalized Linear version; eight. Survival research; nine. Longitudinal information and Their research; 10.

Read e-book online Stochastic partial differential equations: six perspectives PDF

The sphere of Stochastic Partial Differential Equations (SPDEs) is likely one of the so much dynamically constructing components of arithmetic. It lies on the pass component of likelihood, partial differential equations, inhabitants biology, and mathematical physics. the sector is mainly beautiful due to its interdisciplinary nature and the big richness of present and strength destiny functions.

Download e-book for kindle: Bayesians Versus Frequentists: A Philosophical Debate on by Jordi Vallverdú

This publication analyzes the origins of statistical considering in addition to its comparable philosophical questions, equivalent to causality, determinism or likelihood. Bayesian and frequentist ways are subjected to a old, cognitive and epistemological research, making it attainable not to in basic terms examine the 2 competing theories, yet to additionally discover a power resolution.

Extra resources for Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects

Sample text

They are too disparate and they are developing too quickly. Having made that unsexy concession, there does seem to be a good place to start. Many of the new methods are now undergoing a process of unification analogous to the unifications that produced generalized linear models and, even earlier, the projection theory of linear models. The unification of models with random effects — so far — consists of a few competing syntaxes for expressing a large class of models and a method for fitting models expressed in each syntax.

To answer this question, each eGFR was compared separately to iGFR, using the following analysis. I describe the simplest analysis; variants included a covariate describing steroid use and analyses of subsets of subjects. The dataset included two observations (“cases”) per subject per annual visit, one for iGFR and one for eGFR. The mixed linear model fit a straight line in time (visit number) for each combination of a subject and a GFR method (iGFR or eGFR). For each subject these four quantities — a slope and intercept for each method — were treated as an iid draw from a 4-variate normal distribution.

Examples of this unification include Robinson (1991) and Ruppert et al. (2003). This class of models is rich enough to be interesting and close enough to single-error-term linear models to allow many insights and methods to be borrowed or adapted. A theory of richly parameterized linear models needs more than a syntax and a computing method. It needs to explain things that happen when these models are used to analyze data, to provide ways to detect problems, and when possible, to show how to mitigate or avoid those problems.

Download PDF sample

Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects by James S. Hodges

by Mark

Rated 4.92 of 5 – based on 34 votes