Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test statistics across genes. The empirical Bayes methodology in the nonparametric and parametric formulations, as well as closely...

Modern methods of microarray data analysis are biased towards selecting those genes that display the most pronounced differential expression. The magnitude of differential expression does not necessarily indicate biological significance and other criteria are needed to supplement the information...

One of the prevailing ideas in the literature on microarray data analysis is to pool the expression measures across genes and treat them as a sample drawn from some distribution. Several universal laws were proposed to analytically describe this distribution. This idea raises a number of...

Multiple testing analysis, based on clustering methodologies, is usually applied in Microarray Data Analysis for comparisons between pair of groups. In this paper, we generalize this methodology to deal with multiple comparisons among more than two groups obtained from microarray expressions of...

Multiple testing analysis and clustering methodologies are usually applied in microarray data analysis. A combination of both methods to deal with multiple comparisons among groups obtained from microarray expressions of genes is proposed. Assuming normal data, a statistic which depends on...

Many multiple testing procedures make use of the p-values from the individual pairs of hypothesis tests, and are valid if the p-value statistics are independent and uniformly distributed under the null hypotheses. However, it has recently been shown that these types of multiple testing...

Questo lavoro illustra il disegno sperimentale split-plot ponendo l’attenzione allo sviluppo che questo piano sperimentale ha ottenuto negli ultimi anni in ambito tecnologico e ambientale. In particolare, tale disegno sperimentale è qui brevemente introdotto considerando la struttura...

In industrial experimentation there is growing interest in studies that span more than one processing step. Convenience often dictates restrictions in randomization in passing from one processing step to another. When the study encompasses three processing steps, this leads to split-split-plot...

Response surface experiments often involve only quantitative factors, and the response is fit using a full quadratic model in these factors. The term response surface implies that interest in these studies is more on prediction than parameter estimation since the points on the fitted surface are...

Many industrial experiments involve one or more restrictions on the randomization. In such cases, the split-plot design structure, in which the experimental runs are performed in groups, is a commonly used cost-efficient approach that reduces the number of independent settings of the...