Modern technologies are generating ever-increasing amounts of data. Making use these data requires methods that both statistically sound and computationally efficient. Typically, the statistical computational aspects treated separately. In this paper, we propose an approach to entangle two in context regularized estimation. Applying our sparse group-sparse regression, show it can improve on sta...

Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classiication methodology. The performance of many classiication algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input data, and taking a weighted majority vote of the sequence of classiiers thereby produced. We show that this se...

Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hundreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scanner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vectors (collection of signals acquire...

In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.

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Survey data logistic regression analysis, as computationally available in SAS SURVEYLOGISTIC procedure, has been widely conducted in survey research practice. A set of diagnostic statistics in the procedure, borrowed from the logistic regression in generalized linear models, is used for model assessment. However, for survey data, the statistical underpinnings of these statistics may need to be ...

A new and e ective design method is presented for statistical regression functions that belong to the class of mixture models. The class includes the hierarchical mixture of experts (HME) and the normalized radial basis functions (NRBF). Design algorithms based on the maximum likelihood (ML) approach, which emphasize a probabilistic description of the model, have attracted much interest in HME ...

We describe an approach to Statistical Model Checking (SMC) that produces not only an estimate of the probability that specified properties (a.k.a. predicates) are satisfied, but also an “input attribution” for those predicates. We use logistic regression to generate the input attribution as a set of linear and non-linear functions of the inputs that explain conditions under which a predicate i...