An alternative approach to minimize model inadequacy

In this article Bock's (1975) approach is used to fit a class of lower order polynomials to a higher order response function. For a wide class of conditions our fitted models offer greater protection, in some sense, against model inadequacies than the one fitted by Karson, Manson and Hader (1969). However, our approach is applicable to the situations where the assumption of normality about the distribution of the response variable is appropriate.     

Comments on a Paper by Lachenbruch and Flack

Razzaghi (1987) conjectured that a wrong choice of covariance matrix in a restricted linear model results in loss of efficiency. This conjecture was proved correct by Kabe and Gupta for a wrong choice of constant covariance matrix. The present paper demonstrates that this loss of efficiency persists even with an estimated covariance matrix, thereby resulting in inefficient estimation, prediction, and confidence intervals.     

A note on generalized ridge estimators

It is shown that a necessary and sufficient condition derived by Farebrother (1984)for a generalized ridge estimator to dominate the ordinary least-squares estimator with respect to the mean-square-error-matrix criterion in the linear regression model admits a similar interpretation as the well known criterion of Toro-Viz-carrondo and Wallace (1968)for the dominance of a restricted least-squares estimator over the ordinary least-squares estimator. Two other properties of the generalized ridge estimators, referring to the concept of admissibility, are also pointed out.     

整数矩阵集上的Fermat方程

设A 是 m 阶整数矩阵,A 是由A 的全体方幂构成的集合.本文证明了:当 m > 9 时,如果A 的行列式｜ A｜ ≠0 ,而且A 的特征根不全为单位根,则A 上的Fermat 方程无解.     

也谈欧氏空间的等角变换

在文[1]的基础上，推出了等角变换的几个新的判别法，并给出了等角变换可对角化的几个充要条件。     

Spacing of information in polynomial regression:a simple solution

Consider a k polynomial regression on a single real variable. If n uncorrelated observations are to be taken in a design with support on more than k+1 points, there is an approximate experiment, ν, with support on k+1 points and n observations such that both designs have the same information matrix for the model. A proof of this result is provided. A method to obtain the approximate design ν is given and illustrated by an example. The source of disagreement between Kiefer (1959) and De La Garza (1954) in the solution of this problem is clarified.     

The limiting distribution of the F-statistic from nonnormal universes

We consider a linear regression model with an unbalanced 1-fold nested error structure, where group effect and error are from nonnormal universes. The limiting distribution of the F-statistic in this model is derived, as the sample size is large and group sizes take values from a finite set of distinct integers. The result is used to approximate the F-distribution quantile and to test the significance of the random effect variance component. Results are also applicable to the F-statistic in the one-way random-effects model. The effects of departure from normality on the F-statistic distribution are given.     

The Kumaraswamy Burr XII distribution: theory and practice

For the first time, a five-parameter distribution, called the Kumaraswamy Burr XII (KwBXII) distribution, is defined and studied. The new distribution contains as special models some well-known distributions discussed in lifetime literature, such as the logistic, Weibull and Burr XII distributions, among several others. We obtain the complete moments, incomplete moments, generating and quantile functions, mean deviations, Bonferroni and Lorenz curves and reliability of the KwBXII distribution. We provide two representations for the moments of the order statistics. The method of maximum likelihood and a Bayesian procedure are adopted for estimating the model parameters. For different parameter settings and sample sizes, various simulation studies are performed and compared to the performance of the KwBXII distribution. Three applications to real data sets demonstrate the usefulness of the proposed distribution and that it may attract wider applications in lifetime data analysis.     

EStimating mahalanobis's distance using bayesian analysis

In this paper we are concerned with the problem of estimating Mahalanobis's distance between two or more populations using Bayesian approach. We first consider the case of two independent univariate normal populations having a common variance. We derive the posterior distribution of the     

Sequential estimation for continuous time finite markov processes

Efficient sequential estimation of the intensity rates of a continuous-time finite Markov process is discussed. An information inequality which gives a lower bound for the variance of an unbiased estimator of a function of the intensity rates is obtained and it is used to define an efficient estimator. All closed efficient sequential sampling schemes are characterized.