Automatic Block-Length Selection for the Dependent Bootstrap

Abstract

We review the different block bootstrap methods for time series, and present them in a unified framework. We then revisit a recent result of Lahiri [Lahiri, S. N. (1999b). Theoretical comparisons of block bootstrap methods, Ann. Statist. 27:386–404] comparing the different methods and give a corrected bound on their asymptotic relative efficiency; we also introduce a new notion of finite-sample “attainable” relative efficiency. Finally, based on the notion of spectral estimation via the flat-top lag-windows of Politis and Romano [Politis, D. N., Romano, J. P. (1995). Bias-corrected nonparametric spectral estimation. J. Time Series Anal. 16:67–103], we propose practically useful estimators of the optimal block size for the aforementioned block bootstrap methods. Our estimators are characterized by the fastest possible rate of convergence which is adaptive on the strength of the correlation of the time series as measured by the correlogram.     

On the behaviour of some transforms of the sample correlation coefficient in samples from the bivariate t and the bivariate X2distribution

The present paper studies the normality of five transformations suggested in the literature to normalize the sample correlation coefficient. The parent populations are the bivariate t and the bivariate X ²The results in the previous work of Subrahmaniam and Gajjar are exploited to assess their performance. The density estimation procedure of Tarter and Kronmal is used to provide empiric support to the asymptotic results     

论商号的转让

商号是商主体用来表示其营业的名称,商号具有人格属性和财产属性。商号的法律属性对商号的转让具有重要意义。随着经济生活的发展,商号的财产性日趋明显,这有利于商号的转让;同时,商号的人格属性与财产性相对独立,也为商号的转让奠定了基础。商号转让的基础、模式、程序、效力在我国现行法律中缺乏相关规定,值得研究探讨。     

Evaluation of different statistical methods using SAS software: an in silico approach for analysis of real-time PCR data

Real-time polymerase chain reaction (PCR) is reliable quantitative technique in gene expression studies. The statistical analysis of real-time PCR data is quite crucial for results analysis and explanation. The statistical procedures of analyzing real-time PCR data try to determine the slope of regression line and calculate the reaction efficiency. Applications of mathematical functions have been used to calculate the target gene relative to the reference gene(s). Moreover, these statistical techniques compare C_t (threshold cycle) numbers between control and treatments group. There are many different procedures in SAS for real-time PCR data evaluation. In this study, the efficiency of calibrated model and delta delta C_t model have been statistically tested and explained. Several methods were tested to compare control with treatment means of C_t. The methods tested included t-test (parametric test), Wilcoxon test (non-parametric test) and multiple regression. Results showed that applied methods led to similar results and no significant difference was observed between results of gene expression measurement by the relative method.     

Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints

Adaptive designs for multi-armed clinical trials have become increasingly popular recently because of their potential to shorten development times and to increase patient response. However, developing response-adaptive designs that offer patient-benefit while ensuring the resulting trial provides a statistically rigorous and unbiased comparison of the different treatments included is highly challenging. In this paper, the theory of Multi-Armed Bandit Problems is used to define near optimal adaptive designs in the context of a clinical trial with a normally distributed endpoint with known variance. We report the operating characteristics (type I error, power, bias) and patient-benefit of these approaches and alternative designs using simulation studies based on an ongoing trial. These results are then compared to those recently published in the context of Bernoulli endpoints. Many limitations and advantages are similar in both cases but there are also important differences, specially with respect to type I error control. This paper proposes a simulation-based testing procedure to correct for the observed type I error inflation that bandit-based and adaptive rules can induce.     

On some classifiers based on multivariate ranks

Non parametric approaches to classification have gained significant attention in the last two decades. In this paper, we propose a classification methodology based on the multivariate rank functions and show that it is a Bayes rule for spherically symmetric distributions with a location shift. We show that a rank-based classifier is equivalent to optimal Bayes rule under suitable conditions. We also present an affine invariant version of the classifier. To accommodate different covariance structures, we construct a classifier based on the central rank region. Asymptotic properties of these classification methods are studied. We illustrate the performance of our proposed methods in comparison to some other depth-based classifiers using simulated and real data sets.     

A Modified Hall-Padmanabhan Test for the Homogeneity of Scales

We consider a simple adaptive test for the homogeneity of scales due to Hall and Padmanabhan (1997 Hall , P. , Padmanabhan , A. R. ( 1997 ). Adaptive inference for the two-sample scale problem . Technometrics 23 : 351 – 361 . [Google Scholar]) which is based on the ratio of adaptively trimmed sample variances. We develop a modification of this test that has the form of a combined bootstrap test and that is obtained within the nonparametric combination of dependent tests framework (Pesarin, 2001 Pesarin , F. ( 2001 ). Multivariate Permutation Tests with Applications in Biostatistics . Chichester : Wiley . [Google Scholar]). We considered other approaches for the combination of tests concluding that the Liptak method is the most suitable one for the problem at hand. We compare the modified test with the original one in terms of robustness of significance level and power in a simulation study which considers distributions that range from symmetric to skewed and from light to heavy tailed ones. We show that the modified test is more powerful than the original one under heavier than normal tailed and very skewed distributions. A practical application to detect difference in scale of energy intake of lactating and non pregnant and not lactating women is presented.