Optimal designs for response functions with a downturn

In many toxicological assays, interactions between primary and secondary effects may cause a downturn in mean responses at high doses. In this situation, the typical monotonicity assumption is invalid and may be quite misleading. Prior literature addresses the analysis of response functions with a downturn, but so far as we know, this paper initiates the study of experimental design for this situation. A growth model is combined with a death model to allow for the downturn in mean doses. Several different objective functions are studied. When the number of treatments equals the number of parameters, Fisher information is found to be independent of the model of the treatment means and on the magnitudes of the treatments. In general, A- and D_A-optimal weights for estimating adjacent mean differences are found analytically for a simple model and numerically for a biologically motivated model. Results on c-optimality are also obtained for estimating the peak dose and the EC₅₀ (the treatment with response half way between the control and the peak response on the increasing portion of the response function). Finally, when interest lies only in the increasing portion of the response function, we propose composite D-optimal designs.     

Optimal designs for tumor regrowth models

Most growth curves can only be used to model the tumor growth under no intervention. To model the growth curves for treated tumor, both the growth delay due to the treatment and the regrowth of the tumor after the treatment need to be taken into account. In this paper, we consider two tumor regrowth models and determine the locally D- and c-optimal designs for these models. We then show that the locally D- and c-optimal designs are minimally supported. We also consider two equally spaced designs as alternative designs and evaluate their efficiencies.     

Looking for max-semistability: A new test for the extreme value condition

To test the extreme value condition, Cramér-Von Mises type tests were recently proposed by Drees et al. (2006) and Dietrich et al. (2002). Hüsler and Li (2006) presented a simulation study on the behavior of these tests and verified that they are not robust for models in the domain of attraction of a max-semistable distribution function. In this work we develop a test statistic that distinguishes quite well distribution functions which belong to a max-stable domain of attraction from those in a max-semistable one. The limit law is deduced and the results from a numerical simulation study are presented.     

Nonlinear regression modeling via the lasso-type regularization

We consider the problem of constructing nonlinear regression models with Gaussian basis functions, using lasso regularization. Regularization with a lasso penalty is an advantageous in that it estimates some coefficients in linear regression models to be exactly zero. We propose imposing a weighted lasso penalty on a nonlinear regression model and thereby selecting the number of basis functions effectively. In order to select tuning parameters in the regularization method, we use a deviance information criterion proposed by Spiegelhalter et al. (2002), calculating the effective number of parameters by Gibbs sampling. Simulation results demonstrate that our methodology performs well in various situations.     

ROBUST ESTIMATION OF SMALL‐AREA MEANS AND QUANTILES

Small‐area estimation techniques have typically relied on plug‐in estimation based on models containing random area effects. More recently, regression M‐quantiles have been suggested for this purpose, thus avoiding conventional Gaussian assumptions, as well as problems associated with the specification of random effects. However, the plug‐in M‐quantile estimator for the small‐area mean can be shown to be the expected value of this mean with respect to a generally biased estimator of the small‐area cumulative distribution function of the characteristic of interest. To correct this problem, we propose a general framework for robust small‐area estimation, based on representing a small‐area estimator as a functional of a predictor of this small‐area cumulative distribution function. Key advantages of this framework are that it naturally leads to integrated estimation of small‐area means and quantiles and is not restricted to M‐quantile models. We also discuss mean squared error estimation for the resulting estimators, and demonstrate the advantages of our approach through model‐based and design‐based simulations, with the latter using economic data collected in an Australian farm survey.     

分位数回归及应用简介

文章介绍了分位数回归法的概念、算法及主流统计软件R和SAS计算时的语法,并通过实例与以普通最小二乘法为基础的线性回归进行了对比,展现了分位数回归的巨大魅力。     

股指期货最优套期保值与绩效评价——基于分位数回归的实证研究

针对股指期货套期保值的问题,利用分位数回归方法对沪深300指数中权重占前两位的股票的最优套期保值比率进行了实证测算和绩效评价,实证结果表明：运用分位数回归计算得到的最优套期保值比率都大于0.99,在不同分位点上,期货收益对现货收益的影响大小及变动情况存在明显差异。     

实现蔡氏电路族的两种新的规范电路

本文介绍了两种新的规范电路。导出了计算这两种规范电路参数的显式公式,还推广了两位学者的有关定理,使之也适用于新的规范电路。本文设计的规范电路表明,实现蔡氏电路族的规范电路不是唯一的。而其中改造的蔡氏电路也许是最好的一种。     

一类非线性滞后微分方程的振荡性

本文讨论了一阶非线性滞后型微分方程组■，（1）所有解振荡的充分条件，发展了文[1]的方法，并推广强化了文[1]的有关结论。     

中国农村贫困人口多维特征分析

利用中国综合社会调查（CGSS）数据,从个体的基本特征、家庭特征、社会特征和其他特征4个维度,采用二值Logit模型,考察这些特征变量对于农村人口陷入贫困可能性的影响;并进一步通过分位数回归模型比较分析这些特征变量对农村贫困人口和非贫困人口不同收入水平的影响。研究发现,在中国农村,具有以下几个典型特征的人群陷入贫困的可能性较大：老年人、受教育程度较低（自身、配偶或父母）、身体健康状况较差、少数民族、未婚状态、家庭抚养负担过重、无宗教信仰、没有非农工作、周工作时间较短、使用电视或互联网频率较低（这里理解为获取与农业生产和非农就业相关信息）。此外,比较分析结果表明,对于贫困人口和非贫困人口,不同维度下特征变量对收入水平影响的大小和方向存在较大差异。