# 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...

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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.