Interval Estimate of Binomial Parameter p: What is (Relatively) New?

The present work follows up the ROBUST 2006 paper where various types of confidence intervals for binomial parameter p have been exposed. The coverage probability cannot equal the nominal confidence level 1-alpha in the whole domain [0, 1]. This leads to dilemmas (is the coverage of at least 1-alpha a must, or is it better to approximate 1-alpha from both sides?), and to multiplicity of proposals of confidence interval types. The present work extends the scope of the previous paper by such generalizations of "ordinary" confidence intervals that enable a constant coverage, namely by the randomized confidence intervals (introduced several decades ago), and by the relatively new idea of the fuzzy confidence intervals

Classification and Regression Forests

Classification forest is a classification model constructed by combinaning several classification trees. A predictor vector is assigned a class by each of the trees, and the overall classification function is given by majority voting. Similarly, a regression forest consists of several regression trees, and the overall regression function is defined as a weighted average of regression functions of individual trees. Brief explanations of some forest construction methods, namely of bagging, boosting, arcing and Random Forests, are given

Classification and Regression Forests.

Classification forest is a classification model constructed by combinaning several classification trees. A predictor vector is assigned a class by each of the trees, and the overall classification function is given by majority voting. Similarly, a regression forest consists of several regression trees, and the overall regression function is defined as a weighted average of regression functions of individual trees. Brief explanations of some forest construction methods, namely of bagging, boosting, arcing and Random Forests, are given

Matematicke metody hodnoceni zmeny stavu v lekarskem vyzkumu

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

O intervalových odhadech pravděpodobností, zvláště malých

Práce se zabývá modifikací Clopper-Pearsonova konfidenčního intervalu pro parametr binomického rozdělení při nulovém nebo plném počtu úspěchů. Ukazuje se, že modifikace, která má stále své přívržence, je nekorektní. Dále jsou připomenuty některé lepší alternativy k nejběžněji používaným typům konfidenčních intervalů