In this study, slurry polymerization kinetics of ethylene with TiCl4/Mg(OEt)2/AlR3 Ziegler-Natta catalysts in various conditions using the model of sum square error (SSE) (method I) and model of least square error (LSE) (Method II) was investigated. For this purpose the molecular weight distributions of the samples were deconvoluted to the minimum number of Flor...

Frane (2015) pointed out the difference between per-family and familywise Type I error control and how different multiple comparison procedures control one method but not necessarily the other. He then went on to demonstrate in the context of a two group multivariate design containing different numbers of dependent variables and correlations between variables how the per-family rate inflates be...

This work studies multiple hypothesis testing in the setting when we obtain data sequentially and may choose when to stop sampling. We summarize the notion of a sequential pvalue (one that can be continually updated and still maintain a type I error guarantee) and provide several examples from the literature. This tool allows us to convert fixedhorizon step-up or step-down multiple hypothesis t...

MOTIVATION The gene expression intensity information conveyed by (EST) Expressed Sequence Tag data can be used to infer important cDNA library properties, such as gene number and expression patterns. However, EST clustering errors, which often lead to greatly inflated estimates of obtained unique genes, have become a major obstacle in the analyses. The EST clustering error structure, the relati...

The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false negative) rates. However, in the analysis of ...