The purpose of this study was to examine the Practice Difficulty on exam performance for each of the four exam given during the semester. Practice Difficulty was a 3-condition variable practice problems were either about the same difficulty as the exam problems (=1), they were easier than the exam problems (=2), or they were more difficult than the exam problems (=3). Different sections of the ...

Given k independent samples of functional data the problem of testing the null hypothesis of equality of their respective mean functions is considered. So the setting is quite similar to that of the classical one-way anova model but the k samples under study consist of functional data. A simple natural test for this problem is proposed. It can be seen as an asymptotic version of the well-known ...

ANOVA provides a general approach to the analysis of single and multiple factor experiments on both one- and two-color microarray platforms. Mixed model ANOVA is important because in many microarray experiments there are multiple sources of variation that must be taken into consideration when constructing tests for differential expression of a gene. The genome is large, and the signals of expre...

Smoothing Spline ANOVA is a statistical modeling algorithm based on a function decomposition similar to the classical analysis of variance (ANOVA) decomposition and the associated notions of main effect and interaction. It represents a suitable screening technique for detecting important variables (Variable Screening) in a given dataset. We present the mathematical background together with poss...

We show that the lower-order terms in the ANOVA decomposition of a function f(x) := max(φ(x), 0) for x ∈ [0, 1], with φ a smooth function, may be smoother than f itself. Specifically, f in general belongs only toW d,∞, i.e., f has one essentially bounded derivative with respect to any component of x, whereas, for each u ⊆ {1, . . . , d}, the ANOVA term fu (which depends only on the variables xj...