An economic model for the quality selection problem

The object of an economic model for the quality selection problem is to select the best parameter value for an input quality characteristic (X) so that the quality loss incurred on an output quality characteristic (Y) is minimized. The relation function between Y and X is assumed to be known throughout the article. In the work of Taguchi's experimentations, the selection of best parameter values is solved by two-step optimization when Y is adjustable. In the article, it is further extended to the case where Y is non-adjustable. An economic quality selection model with a general relation function is proposed based on a Taylor-series method for both adjustable and non-adjustable cases. A special case of a quality selection model with a quadratic relation function is also studied. A circuit example provided by Taguchi is presented to illustrate the use of this model.     

从墓志看宋代士人家庭的择偶行为

墓志的性质在宋代由哀悼文学转向传记文学，更多地记载墓主个人的事迹。本文通过墓志资料考察宋代士人家庭的择偶行为，进一步了解长辈为子女择偶的状况以及当时社会流行的择偶价值观。     

Bootstrap method in ranking and slippage problems

In this paper, the bootstrap method of Efron (1979) is given for a ranking and a slippage problem, where the ranking (or slippage) is with respect to the mean of the distributions. The method is also applied to obtain a confidence interval for the largest mean.     

On selecting the best population

This paper deals with the problem of selecting the “best” population from a given number of populations in a decision theoretic framework. The class of selection rules considered is based on a suitable partition of the sample space. A selection rule is given which is shown to have certain optimum properties among the selection rules in the given class for a mal rules are known.     

Variable Selection via Regression Trees in the Presence of Irrelevant Variables

Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many irrelevant variables and the number of predictors exceeds the number of observations. We propose the multistep regression tree with adaptive variable selection to handle this problem. The variable selection step and the fitting step comprise the multistep method.

The multistep generalized unbiased interaction detection and estimation (GUIDE) with adaptive forward selection (fg) algorithm, as a variable selection tool, performs better than some of the well-known variable selection algorithms such as efficacy adaptive regression tube hunting (EARTH), FSR (false selection rate), LSCV (least squares cross-validation), and LASSO (least absolute shrinkage and selection operator) for the regression problem. The results based on simulation study show that fg outperforms other algorithms in terms of selection result and computation time. It generally selects the important variables correctly with relatively few irrelevant variables, which gives good prediction accuracy with less computation time.     

Penalized proportion estimation for non parametric mixture of regressions

Abstract

In this article, we propose a penalized local log-likelihood method to locally select the number of components in non parametric finite mixture of regression models via proportion shrinkage method. Mean functions and variance functions are estimated simultaneously. We show that the number of components can be estimated consistently, and further establish asymptotic normality of functional estimates. We use a modified EM algorithm to estimate the unknown functions. Simulations are conducted to demonstrate the performance of the proposed method. We illustrate our method via an empirical analysis of the housing price index data of United States.     

移动互联网背景下电信运营商产业链延伸合作模式及策略研究

移动互联网的快速发展和市场竞争的加剧促使电信运营商开始转型,加强产业链合作,服务提供商(SP)、内容提供商(CP)等互联网企业和终端制造商是电信运营商寻求合作的重要方向.文章分析了移动互联网背景下电信运营商开展产业链延伸合作的动因;提出了平台开放型、股权投资型、战略联盟型等三种电信运营商和SP/CP的合作模式,以及合约合作型、深入定制型、运营商自建型等三种电信运营商和终端制造商的合作模式;并进一步分析了电信运营商与SP/CP以及终端制造商合作的模式选择策略,以期为中国电信运营商开展产业链合作提供借鉴和参考.     

Nonparametric Estimation of the Number of Drug Users in Hong Kong Using Repeated Multiple Lists

We update a previous approach to the estimation of the size of an open population when there are multiple lists at each time point. Our motivation is 35 years of longitudinal data on the detection of drug users by the Central Registry of Drug Abuse in Hong Kong. We develop a two‐stage smoothing spline approach. This gives a flexible and easily implemented alternative to the previous method which was based on kernel smoothing. The new method retains the property of reducing the variability of the individual estimates at each time point. We evaluate the new method by means of a simulation study that includes an examination of the effects of variable selection. The new method is then applied to data collected by the Central Registry of Drug Abuse. The parameter estimates obtained are compared with the well known Jolly–Seber estimates based on single capture methods.     

Model averaging inconcentration–QT analyses

This article describes how a frequentist model averaging approach can be used for concentration–QT analyses in the context of thorough QTc studies. Based on simulations, we have concluded that starting from three candidate model families (linear, exponential, and Emax) the model averaging approach leads to treatment effect estimates that are quite robust with respect to the control of the type I error in nearly all simulated scenarios; in particular, with the model averaging approach, the type I error appears less sensitive to model misspecification than the widely used linear model. We noticed also few differences in terms of performance between the model averaging approach and the more classical model selection approach, but we believe that, despite both can be recommended in practice, the model averaging approach can be more appealing because of some deficiencies of model selection approach pointed out in the literature. We think that a model averaging or model selection approach should be systematically considered for conducting concentration–QT analyses. Copyright © 2016 John Wiley & Sons, Ltd.     

基于多元词组和数据流聚类的热点话题动态发现

本文主要通过改进的TF-IDF算法和多元词组动态构建来选择特征关键词,并利用CluStream数据流聚类方法,实现文本主题的动态发现.实验表明,该方法可以较好地发现海量文本信息中不断变化的主题信息,从而达到推荐关联主题、动态监测舆情等目的.