Analysis of variance for functional data by Jin-Ting Zhang

By Jin-Ting Zhang

"Preface sensible info research has been a favored statistical learn subject for the final 3 a long time. sensible info are usually got through staring at a couple of matters over the years, area or different continua densely. they're usually gathered from numerous learn parts, together with audiology, biology, kid's progress reviews, ergonomics, environmentology, me- teorology, and women's future health experiences among Read more...

Show description

Read Online or Download Analysis of variance for functional data PDF

Similar probability & statistics books

Theories in Probability: An Examination of Logical and Qualitative Foundations (Advanced Series on Mathematical Psychology)

Common likelihood conception has been an tremendously profitable contribution to trendy technology. despite the fact that, from many views it really is too slim as a normal conception of uncertainty, quite for matters related to subjective uncertainty. This first-of-its-kind e-book is based mostly on qualitative methods to probabilistic-like uncertainty, and contains qualitative theories for a standard idea in addition to a number of of its generalizations.

An Introduction to Statistical Inference and Its Applications

Emphasizing ideas instead of recipes, An advent to Statistical Inference and Its purposes with R presents a transparent exposition of the equipment of statistical inference for college kids who're pleased with mathematical notation. a variety of examples, case reports, and routines are integrated. R is used to simplify computation, create figures, and draw pseudorandom samples—not to accomplish whole analyses.

Probability on Discrete Structures

Such a lot chance difficulties contain random variables listed by means of area and/or time. those difficulties often have a model within which area and/or time are taken to be discrete. This quantity bargains with components within which the discrete model is extra average than the continual one, maybe even the one one than could be formulated with out advanced buildings and equipment.

Introduction to Bayesian Estimation and Copula Models of Dependence

Provides an creation to Bayesian facts, offers an emphasis on Bayesian tools (prior and posterior), Bayes estimation, prediction, MCMC,Bayesian regression, and Bayesian research of statistical modelsof dependence, and contours a spotlight on copulas for probability administration creation to Bayesian Estimation and Copula versions of Dependence emphasizes the purposes of Bayesian research to copula modeling and equips readers with the instruments had to enforce the tactics of Bayesian estimation in copula types of dependence.

Extra resources for Analysis of variance for functional data

Sample text

Therefore, in these situations, functional data analysis (FDA) is more natural. In fact, Ramsay and Silverman (2002, 2005) provide many nice FDA tools to solve the aforementioned problems. In Chapters 2 and 3 of this book, we also provide some tools to overcome difficulties encountered in the first two situations while other chapters of the book provide methodologies to overcome difficulties encountered in the third situation. 4 17 Overview of the Book In this book, we aim to conduct a thorough survey on the topics of hypothesis testing in the context of analysis of variance for functional data and give a systematic treatment of the methodologies.

One can also find a functional regression model to predict the right elbow angle curve y(t), t ∈ [0, 1] using the coordinates (a, b, c) of a target, where a represents the coordinate in the left-to-right direction, b represents the coordinate in the close-to-far direction, and c represents the coordinate in the downto-up direction. 1) where yi (t) and vi (t) denote the ith response and location-effect curves over time, respectively, (ai , bi , ci ) denotes the coordinates of the target associated with the ith angle curve, and βr (t), r = 0, 1, · · · , 9 are unknown coefficient functions.

6 We now apply the regression spline method to fit the fifth Canadian temperature curve. 14) with k = 2. For each of the Canadian temperature curves, there are 365 measurements. 6. 7 shows the GCV curve for such a quadratic regression spline fit. It is seen that the GCV rule favors a large number of interior knots in this regression spline fit and suggests that the optimal number of knots is K = 48. m” limits the large number of interior knots to 50; otherwise, a larger number of interior knots is also possible.

Download PDF sample

Rated 4.56 of 5 – based on 38 votes