Tests for the umbrella alternative under normality

Consider a one-way layout of the analysis of variance assuming independence, normality, and homogeneity of variance. Test the null hypothesis H_o that the means, j., of each of Amp; columns, i = 1,…, k are equal versus the alternative that they follow an umbrella pattern. That is, the alternative is H₁-H₀ where H₁: μ₁> μ₂>… > μk, and m is known. We derive a class of tests which are unbiased and lie in a nontrivial complete class. We recommend specific tests within the class. A simulation of the power functions of some tests is contrasted with the simulated power function of the likelihood ratio test.     

Nonparametric estimation for survival data with censoring indicators missing at random

In this paper, we consider the problem of hazard rate estimation in the presence of covariates, for survival data with censoring indicators missing at random. We propose in the context usually denoted by MAR (missing at random, in opposition to MCAR, missing completely at random, which requires an additional independence assumption), nonparametric adaptive strategies based on model selection methods for estimators admitting finite dimensional developments in functional orthonormal bases. Theoretical risk bounds are provided, they prove that the estimators behave well in term of mean square integrated error (MISE). Simulation experiments illustrate the statistical procedure.     

Time-of-Day Electricity Consumption Response to Temperature and the Ownership of Air Conditioning Appliances

This article is concerned with the endogeneity of air conditioning appliance ownership in the consumption of electricity by time of day (TOD). Both TOD consumption and air conditioning appliance ownership depend on temperature level and on household responsiveness to temperature variation, as well as on other measured household characteristics. The article presents an appropriate econometric methodology and applies it to obtain estimates and perform tests based on data from Southern California Edison's Residential TOD Pricing Experiment. The authors emphasize the implications for estimated consumption price elasticities.     

Simplified Maximum Likelihood Inference Based on the Likelihood Decomposition for Missing Data

In this paper, we propose an estimation method when sample data are incomplete. We decompose the likelihood according to missing patterns and combine the estimators based on each likelihood weighting by the Fisher information ratio. This approach provides a simple way of estimating parameters, especially for non‐monotone missing data. Numerical examples are presented to illustrate this method.     

Estimation Of A Survival Function With Doubly Censored Data And Dirichlet Process Prior Knowledge On The Observable Variable

This article presents a nonparametric Bayesian procedure for estimating a survival curve in a proportional hazard model when some of the data are censored on the left and some are censored on the right. The method works under the assumption that there is a Dirichlet process prior knowledge on the observable variable. Strong consistency of the estimator is proved and an example is given. To finish some simulation is presented to analyze the estimator.     

A new discrete distrlbution,with applications to survival,dispersal and dispersion

We describe a new discrete probability distribution with several useful properties for the analysis and modelling of survival processes and dispersion. First, the model can be used to describe survival processes with monotonically decreasing, constant, or increasing hazard functions, simply by tuning one parameter. Also, the model can describe counts that are overdispersed (contagious) or underdispersed, since the variance can exceed, equal, or be less than the mean. All of these properties are demonstrated both theoretically and with ecological examples, using ad-hoc parameter estimation techniques. Finally, the equations are tractable compared with, say, the negative binomial, and easily incorporated into larger models.     

Robust minimum distance inference based on combined distances

The minimum disparity estimators proposed by Lindsay (1994) for discrete models form an attractive subclass of minimum distance estimators which achieve their robustness without sacrificing first order efficiency at the model. Similarly, disparity test statistics are useful robust alternatives to the likelihood ratio test for testing of hypotheses in parametric models; they are asymptotically equivalent to the likelihood ratio test statistics under the null hypothesis and contiguous alternatives. Despite their asymptotic optimality properties, the small sample performance of many of the minimum disparity estimators and disparity tests can be considerably worse compared to the maximum likelihood estimator and the likelihood ratio test respectively. In this paper we focus on the class of blended weight Hellinger distances, a general subfamily of disparities, and study the effects of combining two different distances within this class to generate the family of “combined” blended weight Hellinger distances, and identify the members of this family which generally perform well. More generally, we investigate the class of "combined and penal-ized" blended weight Hellinger distances; the penalty is based on reweighting the empty cells, following Harris and Basu (1994). It is shown that some members of the combined and penalized family have rather attractive properties     

Stochastic Comparisons and Dependence of Spacings from Two Samples of Exponential Random Variables

Let X ₁,X ₂,…,X _n be independent exponential random variables such that X _i has hazard rate λ for i = 1,…,p and X _j has hazard rate λ* for j = p + 1,…,n, where 1 ≤ p < n. Denote by D _i:n (λ, λ*) = X _i:n  ? X _i?1:n the ith spacing of the order statistics X _1:n  ≤ X _2:n  ≤ ··· ≤ X _n:n , i = 1,…,n, where X _0:n ≡ 0. It is shown that the spacings (D _1,n ,D _2,n ,…,D _n:n ) are MTP₂, strengthening one result of Khaledi and Kochar (2000), and that (D _1:n (λ₂, λ*),…,D _n:n (λ₂, λ*)) ≤ _lr (D _1:n (λ₁, λ*),…,D _n:n (λ₁, λ*)) for λ₁ ≤ λ* ≤ λ₂, where ≤ _lr denotes the multivariate likelihood ratio order. A counterexample is also given to show that this comparison result is in general not true for λ* < λ₁ < λ₂.     

Change Point Detection in a General Class of Distributions

ABSTRACT

We consider the change point problem in a general class of distributions, and derive a test statistic T _n which reduces to the statistic obtained by Kander and Zacks (1966 Kander , Z. , Zacks , S. ( 1966 ). Test procedure for possible changes in parameter of statistical distributions occurring at unknown time points. Ann. Math. Statist. 37 : 1196 – 1210 . [CSA] [Crossref] , [Google Scholar]) for the exponential family. Properties of the test, including its asymptotic distribution, are discussed.     

Estimation of Sib–Sib Correlation via a Kotz-Type Density Function

We estimate sib–sib correlation by maximizing the log-likelihood of a Kotz-type distribution. Using extensive simulations we conclude that estimating sib–sib correlation using the proposed method has many advantages. Results are illustrated on a real life data set due to Galton. Testing of hypothesis about this correlation is also discussed using the three likelihood based tests and a test based on Srivastava's estimator. It is concluded that score test derived using Kotz-type density performs the best.