Type I Error Rates for Multiple Comparison Procedures with Dependent Data

One of the standard assumptions in analysis of variance is independence of observations. This article determines the effect of dependence of observations on several multiple comparison procedures. The correlation structure that is considered is one in which observations within groups have a certain correlation, say ρ₁, and observations in different groups have another correlation, say ρ₂.     

基于行业集中度与区位熵的宝鸡市产业集聚度测度研究

在对现有产业集聚度测度方法述评的基础上,根据宝鸡市产业发展历程和现实情况,选择绝对测度指标（行业集中度）和相对测度指标（区位熵）对宝鸡市一级目录产业和二级目录产业的集聚状况分别进行测算.结果显示：依据区位熵和行业集中度指数反映的宝鸡市工业产业集聚情况基本吻合,同时也发现宝鸡市产业受资源禀赋和产业布局的影响,产品结构单一、重复生产严重、产业关联度低,配套协作率低等问题,以期为宝鸡市产业向集群化发展提供理论依据和对策建议.     

Market potential, European Union and growth

The successive enlargements of the European Union have implied an important increase of the market where European firms can supply their products. In this paper we analyze the influence of this process on the economic growth of EU members by including the market potential as a scale indicator in a Solow-type model. The main results are: first, the integration in the EU, specially for new members, explain a substantial fraction of subsequent growth (between 15% and 40%); second, this effect diminishes over time; third, the GDP of new members appears to have a greater positive influence than its population; and fourth, peripheral countries and those less open to trade are in a worse position to appropriate such benefits.     

关于西安旅游市场机会指数的研究

根据市场机会指数的理论,市场机会指数Ij、市场占有率R和市场需求满足度Qj/Dj三者的关系确定细分市场产品竞争策略的方法,并在此基础上对西安旅游细分市场进行定性和定量分析,提出具体的措施.     

Testing in semiparametric models with interaction, with applications to gene–environment interactions

Summary.  Motivated from the problem of testing for genetic effects on complex traits in the presence of gene–environment interaction, we develop score tests in general semiparametric regression problems that involves Tukey style 1 degree-of-freedom form of interaction between parametrically and non-parametrically modelled covariates. We find that the score test in this type of model, as recently developed by Chatterjee and co-workers in the fully parametric setting, is biased and requires undersmoothing to be valid in the presence of non-parametric components. Moreover, in the presence of repeated outcomes, the asymptotic distribution of the score test depends on the estimation of functions which are defined as solutions of integral equations, making implementation difficult and computationally taxing. We develop profiled score statistics which are unbiased and asymptotically efficient and can be performed by using standard bandwidth selection methods. In addition, to overcome the difficulty of solving functional equations, we give easy interpretations of the target functions, which in turn allow us to develop estimation procedures that can be easily implemented by using standard computational methods. We present simulation studies to evaluate type I error and power of the method proposed compared with a naive test that does not consider interaction. Finally, we illustrate our methodology by analysing data from a case–control study of colorectal adenoma that was designed to investigate the association between colorectal adenoma and the candidate gene NAT2 in relation to smoking history.     

The performance of several statistical tests for seasonality in monthly data

The type I and II error rates of several statistical tests for seasonality in monthly data were investigated through a computer simulation study at two nominal significance levels, α=1% and α=5%. Three models were used for the variation: annual sinusoidal; semi—annual sinusoidal; and a curve which is constant in all but three consecutive months of the year, when it exhibits a constant increase (a “one—pulse” model). The statistical tests are compared in terms of the simulation results. These results may be applied to calculate either the sample size required to detect seasonal variation of fixed amplitude or the probability of detecting seasonal variation of variable amplitude with a fixed sample size. A numerical case study is given     

False Discovery Rate Control of Step‐Up‐Down Tests with Special Emphasis on the Asymptotically Optimal Rejection Curve

Abstract. This paper is concerned with exact control of the false discovery rate (FDR) for step‐up‐down (SUD) tests related to the asymptotically optimal rejection curve (AORC). Since the system of equations and/or constraints for critical values and FDRs is numerically extremely sensitive, existence and computation of valid solutions is a challenging problem. We derive explicit formulas for upper bounds of the FDR and show that under a well‐known monotonicity condition, control of the FDR by a step‐up procedure results in control of the FDR by a corresponding SUD procedure. Various methods for adjusting the AORC to achieve finite FDR control are investigated. Moreover, we introduce alternative FDR bounding curves and study their connection to rejection curves as well as the existence of critical values for exact FDR control with respect to the underlying FDR bounding curve. Finally, we propose an iterative method for the computation of critical values.     

The response functions for competing risks

The competing risks data consist of a pair (T,δ) where T≧0 can be interpreted as the failure time and δ as the identifier of the risk causing the failure. In many practical situations, the main interest lies in studying the inter-relationships between the probability structures of the failure time under various risks. Here, we consider two risks and propose the Junctional relationships between the probability structure of (T,δ=1) and (T,δ=2) by time-dependent scale and shape shifts. Also, a model which raptures the relative aging of a unit under the two risks is proposed. The necessary theory for confidence estimation of these shift functions is developed. These techniques are illustrated through several data sets available in the literature.     

Conditional Quantile Estimation for Truncated and Associated Data

Abstract

In survival or reliability studies, it is common to have data which are not only incomplete but weakly dependent too. Random truncation and censoring are two common forms of such data when they are neither independent nor strongly mixing but rather associated. The focus of this paper is on estimating conditional distribution and conditional quantile functions for randomly left truncated data satisfying association condition. We aim at deriving strong uniform consistency rates and asymptotic normality for the estimators and thereby, extend to association case some results stated under iid and α-mixing hypotheses. The performance of the quantile function estimator is evaluated on simulated data sets.     

贝尔油田希 11-72 井区注空气室内实验研究

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