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Structural Equation Modeling, A Bayesian Approach
TLDR
This book would be useful for anyone who uses GenStat and/or R desiring an introduction to applied mixed modeling, and they should certainly have a look, but it should not be the sole resource. Expand
Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models
Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly useful in small areaExpand
Procrustes Problems
TLDR
This book did not live up to my expectations and disappointed in three specific ways, including that the amount of the text specifically devoted to probabilistic reasoning is relatively small compared to the book’s total length. Expand
Generalized bootstrap for estimating equations
We introduce a generalized bootstrap technique for estimators obtained by solving estimating equations. Some special cases of this generalized bootstrap are the classical bootstrap of Efron, theExpand
Distribution-free cumulative sum control charts using bootstrap-based control limits
This paper deals with phase II, univariate, statistical process control when a set of in-control data is available, and when both the in-control and out-of-control distributions of the process areExpand
Sparse Group Lasso: Consistency and Climate Applications
TLDR
In this paper, theoretical statistical consistency of estimators with tree-structured norm regularizers is proved, which proves that the SGL model provides better predictive performance than the current state-of-the-art, remains climatologically interpretable, and is robust in its variable selection. Expand
Causality and pathway search in microarray time series experiment
TLDR
Simulation shows good convergence and accuracy of the algorithm, andsembled network of the genes reveals features of the network that are common wisdom about naturally occurring networks. Expand
Probabilistic Matrix Addition
TLDR
PMA is introduced for modeling real-valued data matrices by simultaneously capturing covariance structure among rows and among columns and it is demonstrated the effectiveness of PMA for missing value prediction and multi-label classification problems. Expand
Generalized bootstrap for estimators of minimizers of convex functions
We introduce a generalized bootstrap technique for estimators obtained by minimizing functions that are convex in the parameter. We establish the consistency of these schemes via representationExpand
Another look at the jackknife: further examples of generalized bootstrap
In this paper we have three main results. (a) We show that all jackknife schemes are special cases of generalised bootstrap. (b) We introduce a new generalised bootstrap technique called DBS toExpand
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