Designing completely randomized experiments to detect ordered treatment effects: an empirical study

In designing experiments the researcher frequently must decide as to how to allocate fixed resources among k factor levels (Cox (1958)). This study investigates the effects on the power of a test caused by changes in: the sample size (n); the number of factor levels (k); the allocation of fixed total observations (N) among k and n: the shift parameter (ø); the type of parent population sampled; and, the type of ordered location alternative involved. Using Monte Carlo methods the powers of eight test procedures specifically devised to detect ordered treatment effects under completely randomized designs were evaluated along with those of the more general one-way F test. The results are of interest to researchers in all fields of application.     

Kernel-based density estimation using censored,truncated or grouped data

Censoring, truncation and grouping represent different but related forms of incompleteness. Methods of producing kernel functions on the incomplete observations are proposed. They involve substituting for or averaging over the incomplete observations. Consistency of the procedures in terms of:the criterion of integrated mean squared error is established and optimal choice of smoothing parameter is achieved.     

Penalized least squares smoothing of two-dimensional mortality tables with imposed smoothness

This paper presents a method to estimate mortality trends of two-dimensional mortality tables. Comparability of mortality trends for two or more of such tables is enhanced by applying penalized least squares and imposing a desired percentage of smoothness to be attained by the trends. The smoothing procedure is basically determined by the smoothing parameters that are related to the percentage of smoothness. To quantify smoothness, we employ an index defined first for the one-dimensional case and then generalized to the two-dimensional one. The proposed method is applied to data from member countries of the OECD. We establish as goal the smoothed mortality surface for one of those countries and compare it with some other mortality surfaces smoothed with the same percentage of two-dimensional smoothness. Our aim is to be able to see whether convergence exists in the mortality trends of the countries under study, in both year and age dimensions.     

金融市场高维波动率的扩展广义正交GARCH模型与参数估计方法研究

本文对Van der Weide(2002)的广义正交GARCH模型进行扩展,提出反映金融资产收益波动性特征,具有"杠杆效应"的广义正交GARCH模型。由于这种扩展的广义正交GARCH模型在高维数据中面临参数估计困难,本文从交互信息理论视角研究模型的参数估计问题,在理论上证明基于交互信息最小化的多元GARCH模型参数估计与基于极大似然函数参数估计的联系和区别,并在提出的扩展广义正交GARCH模型框架下,采用不同的统计技术实现基于交互信息最小化的参数估计方法,避免了传统极大似然函数估计需要事先正确指定标准化残差概率密度函数和高维运算困难,计算效率较高,使多元GARCH模型在高维数据中可以应用。最后,根据全球主要金融市场的15种股票指数数据,通过实证研究对建立的扩展广义正交GARCH模型及其参数估计方法有效性进行评价与检验。实证研究表明了本文提出的扩展广义正交GARCH模型与参数估计方法的优势。     

唢呐的“转音”及解决方法

从明确什么是唢呐演奏中的＂转音＂入手,分析唢呐＂转音＂出现的多种原因及解决方法,提出唢呐超吹时气息力度和双唇控哨力度之间的比例关系,这将有助于演奏者从理性上了解＂转音＂的性质,并在实际演奏中更好地掌握超吹的音准。     

冲击响应谱试验参数的设置

与经典冲击相比,冲击响应谱试验可以更真实地模拟军用设备在使用和运输过程中所遇到的复杂冲击环境.文中介绍了冲击响应谱的基本概念,阐述了冲击响应谱试验参数的设置方法及同一参数不同设置对试验的影响.验证试验结果表明,按此方法进行试验参数设置可获得较高的冲击响应谱试验精度.     

The beta Weibull-geometric distribution

A new five-parameter distribution called the beta Weibull-geometric (BWG) distribution is proposed. The new distribution is generated from the logit of a beta random variable and includes the Weibull-geometric distribution of Barreto-Souza et al. [The Weibull-geometric distribution, J. Stat. Comput. Simul. 81 (2011), pp. 645–657], beta Weibull (BW), beta exponential, exponentiated Weibull, and some other lifetime distributions as special cases. A comprehensive mathematical treatment of this distribution is provided. The density function can be expressed as an infinite mixture of BW densities and then we derive some mathematical properties of the new distribution from the corresponding properties of the BW distribution. The density function of the order statistics and also estimation of the stress–strength parameter are obtained using two general expressions. To estimate the model parameters, we use the maximum likelihood method and the asymptotic distribution of the estimators is also discussed. The capacity of the new distribution are examined by various tools, using two real data sets.     

A Simple New Algorithm for Quadratic Programming with Applications in Statistics

Problems involving estimation and inference under linear inequality constraints arise often in statistical modeling. In this article, we propose an algorithm to solve the quadratic programming problem of minimizing for positive definite Q, where is constrained to be in a closed polyhedral convex cone , and the m × n matrix is not necessarily full row rank. The three-step algorithm is intuitive and easy to code. Code is provided in the R programming language.     

A model-averaging approach for smoothing spline regression

ABSTRACT

This article considers nonparametric regression problems and develops a model-averaging procedure for smoothing spline regression problems. Unlike most smoothing parameter selection studies determining an optimum smoothing parameter, our focus here is on the prediction accuracy for the true conditional mean of Y given a predictor X. Our method consists of two steps. The first step is to construct a class of smoothing spline regression models based on nonparametric bootstrap samples, each with an appropriate smoothing parameter. The second step is to average bootstrap smoothing spline estimates of different smoothness to form a final improved estimate. To minimize the prediction error, we estimate the model weights using a delete-one-out cross-validation procedure. A simulation study has been performed by using a program written in R. The simulation study provides a comparison of the most well known cross-validation (CV), generalized cross-validation (GCV), and the proposed method. This new method is straightforward to implement, and gives reliable performances in simulations.     

Parameter Estimation Algorithms for the Hyper-Gamma Distribution Class

Microcomputer-based algorithms for the estimation of the parameters shift, scale, initial and terminal shape of the hyper–Gamma distribution class are presented. They are based on the moment equations and on the logarithmic likelihood function (LLF) associated with the hyper-Gamma density. The maximum–likelihood approach is implemented by means of the derivative equations resulting from the LLF and, independently, by means of direct optimization of the LLF. Program options include estimation of (i) four parameters, (ii) three parameters (shift known), and (iii) two parameters (shift known, initial shape zero). A program diskette with user's guide will be made available upon request.