Simple least squares estimation versus best linear unbiased prediction

Necessary and sufficient conditions are developed for the simple least squares estimator to coincide with the best linear unbiased predictor. The conditions obtained are valid for a general linear model and are generalizations of the condition given by Watson (1972). Also, as a preliminary result, a new representation of the best linear unbiased predictor is established.     

Design of a rotation scheme for a stratified multi-stage sample

A rotation scheme for a stratified multi-stage sample, discussed in this paper, was designed to statisfy the following conditions: (i) there is a constraint on the number of units that can be replaced in each round, and (ii) it is relatively inexpensive to increase the sample size gradually. An example of these conditions was observed in the development of a plan for measuring the accuracy of the billing process of a telephone company. Estimators of the population proportion of elements that possess a specified characteristic are also derived. Each estimator is a weighted average of the corresponding estimates based on the retained units from the original sample and on the new units, where the weight of the estimate based on the new units increases over time. While this rotation scheme is discussed in connection with the billing accuracy of a telephone company, the methodology can be applied to other similar problems.     

一类中立型偏微分方程的振动性

本文研究了一类中立型偏微分方程(?)~2 /(?)t~2[u(x,t)+sum from i=1 to m(λ_i(t)u(x,t-τ_i)])+(?)/(?)t[u(x,t)+sum from i=1 to m(λ_i(t)u(x,t-τ_i)])+P(x,t)u(x,t)+sum from j=1 to m_1(P_j(x,t)u(x,t-δ_j))=△u(x,t)+sum from k=1 to m_2(a_k(t)△u(x,t-p_k)(1)解的振动性,其中(x,t)∈Ω×(0,+∞)≡G,Ω(?)R~n是有界域,(?)Ω逐片光滑,△u=sum from k=1 to n((?)~2/(?)x_k~2u(x,t)),我们获得了方程(1)在不同边界条件下的所有解振动的充分条件,并给出这些充分条件应用的实际例子.     

A MODIFICATION OF THE SEQUENTIAL PROBABILITY RATIO TEST FOR TESTING A NORMAL MEAN

A modification of the sequential probability ratio test is proposed in which Wald's parallel boundaries are broken at some preassigned point of the sample number axis and Anderson's converging boundaries are used prior to that. Read's partial sequential probability ratio test can be considered as a special case of the proposed procedure. As far as 'the maximum average sample number reducing property is concerned, the procedure is as good as Anderson's modified sequential probability ratio test.     

Nearly Optimal Tests When a Nuisance Parameter Is Present Under the Null Hypothesis

This paper considers nonstandard hypothesis testing problems that involve a nuisance parameter. We establish an upper bound on the weighted average power of all valid tests, and develop a numerical algorithm that determines a feasible test with power close to the bound. The approach is illustrated in six applications: inference about a linear regression coefficient when the sign of a control coefficient is known; small sample inference about the difference in means from two independent Gaussian samples from populations with potentially different variances; inference about the break date in structural break models with moderate break magnitude; predictability tests when the regressor is highly persistent; inference about an interval identified parameter; and inference about a linear regression coefficient when the necessity of a control is in doubt.     

Employee Misuse of Information Technology Resources: Testing a Contemporary Deterrence Model

Recent research in information systems and operations management has considered the positive impacts of information technology (IT). However, an undesirable side effect of firms’ increasing reliance on IT to support the distribution and delivery of goods and services to customers is a greater exposure to a diverse set of IT security risks. One such risk is intentional employee misuse of technology resources. In this article, we draw upon modern deterrence frameworks to develop a predictive model of technology misuse intention that incorporates formal and informal sanctions as well as employment context factors. The model specifies previously untested relationships between formal and informal sanctions, thereby providing fresh insight into the role of sanctions in deterring technology misuse in organizations. Our results suggest that a predisposition toward the need for social approval and moral beliefs regarding the behavior are key determinants of technology misuse. Contrary to criminological research that has questioned the relative importance of formal sanctions in the deterrence process, we also found that the threat of formal sanctions has both direct and indirect influences on technology misuse intention. Further, from an employment context standpoint, employees who spend more working days away from the office (i.e., “virtual” mode) appear more inclined to misuse their organization's technology resources. The findings have implications for the research and practice of technology management.     

Screening for Multiple Target Detection

We apply statistical selection theory to multiple target detection problems by analyzing the Mahalanobis distances between multivariate normal populations and a desired standard (a known characteristic of a target). We want to achieve the goal of selecting a subset that contains no non target (negative) sites, which entails screening out all non targets. Correct selection (CS) is defined according to this goal. We consider two cases: (1) that all covariance matrices are known; and (2) that all covariance matrices are unknown, including both heteroscedastic and homoscedastic cases. Optimal selection procedures are proposed in order to reach the selection goal. The least favorable configurations (LFC) are found. Tables and figures are presented to illustrate the properties of our proposed procedures. Simulation examples are given to show that our procedures work well. The log-concavity results of the operating characteristic functions are also given.     

Direction dependence in a regression line

In this paper, we derive some simple formulae to express the association between two random variables in the case of a linear relationship, One of these representations, the cube of the correlation coefficient, is given as the ratio of the skewness of the response variable to that of the explanatory variable. This result, along with other expressions of the correlation coefficient presented in this paper, has implications for choosing the response variable in a linear regression modelling.     

Proportional Hazards Model for Multivariate Failure Time Data

Multivariate failure time data also referred to as correlated or clustered failure time data, often arise in survival studies when each study subject may experience multiple events. Statistical analysis of such data needs to account for intracluster dependence. In this article, we consider a bivariate proportional hazards model using vector hazard rate, in which the covariates under study have different effect on two components of the vector hazard rate function. Estimation of the parameters as well as base line hazard function are discussed. Properties of the estimators are investigated. We illustrated the method using two real life data. A simulation study is reported to assess the performance of the estimator.     

On Least Absolute Value Estimators for Poisson Processes

We consider the problem of parameter estimation for an inhomogeneous Poisson process observed on the time interval [0, τ]. We introduce the minimum L ₁-norm estimator of the unknown parameter and study the asymptotical behaviors of the estimates when the number of observations increases. It is established that this estimator is consistent and we show that the corresponding differences converge to certain variables. These limit variables are asymptotically normal as τ tends to infinity.