Discrete Beta-Exponential Distribution

Consider the estimation of the regression parameters in the usual linear model. For design densities with infinite support, it has been shown by Faraldo Roca and González Manteiga [1] Faraldo Roca, P. and González Manteiga, W. 1987. “Efficiency of a new class of linear regression estimates obtained by preliminary nonparametric estimation”. In New Perspectives in Theoretical and Applied Statistics Edited by: Puri, M. L., Vilaplana, J. P. and Wertz, W. 229–242. New York: John Wiley.  [Google Scholar] that it is possible to modify the classical least squares procedure and to obtain estimators for the regression parameters whose MSE's (mean squared errors) are smaller than those of the usual least squares estimators. The modification consists of presmoothing the response variables by a kernel estimator of the regression function. These authors also show that the gain in efficiency is not possible for a design density with compact support. We show that in this case local linear presmoothing does not fix this inefficiency problem, in spite of the well known fact that local linear fitting automatically corrects the bias in the endpoints of the (design density) support. We demonstrate on a theoretical basis how this inefficiency problem can be rectified in the compact design case: we prove that presmoothing with boundary kernels studied in Müller [2] Müller, H.-G. 1991. Smooth optimum kernel estimators near endpoints. Biometrika, 78: 521–530. [Crossref], [Web of Science ®] , [Google Scholar] and Müller and Wang [3] Müller, H.-G. and Wang, J.-L. 1994. Hazard rate estimation under random censoring with varying kernels and bandwidths. Biometrics, 50: 61–76. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] leads to regression estimators which are superior over the least squares estimators. A very careful analytic treatment is needed to arrive at these asymptotic results.     

On the Mixture Proportional Mean Residual Life Model

In this article, we propose a new mixture model induced by the model of proportional mean residual life. Under some appropriate assumptions, it is shown that the mixing and overall variables in the model admit the positive likelihood ratio dependence structure. To see how the overall variable is affected by the stochastic variation of the mixing variable, we study some stochastic comparisons using these variables. Finally, some useful bounds for tail probability of the overall variable for large values of the mixing variable are derived.     

A changepoint statistic with uniform type I error probabilities

ABSTRACT

Likelihood ratio tests for a change in mean in a sequence of independent, normal random variables are based on the maximum two-sample t-statistic, where the maximum is taken over all possible changepoints. The maximum t-statistic has the undesirable characteristic that Type I errors are not uniformly distributed across possible changepoints. False positives occur more frequently near the ends of the sequence and occur less frequently near the middle of the sequence. In this paper we describe an alternative statistic that is based upon a minimum p-value, where the minimum is taken over all possible changepoints. The p-value at any particular changepoint is based upon both the two-sample t-statistic at that changepoint and the probability that the maximum two-sample t-statistic is achieved at that changepoint. The new statistic has a more uniform distribution of Type I errors across potential changepoints and it compares favorably with respect to statistical power, false discovery rates, and the mean square error of changepoint estimates.     

Forecasting a point process with an ARIMA model

ABSTRACT

In fitting a power-law process, we show that the construction of the empirical recurrence rate time series either simplifies the modeling task, or liberates a point process restrained by a key parametric model assumption such as the monotonicity requirement of the intensity function. The technique can be applied to seasonal events occurring in spurts or clusters, because the autoregressive integrated moving average (ARIMA) procedure provides a comprehensive set of tools with great flexibility. Essentially, we consolidate two of the most powerful modeling tools for the stochastic process and time series in the statistical literature to handle counts of events in a Poisson or Poisson-like process.     

Statistical inference for a class of life distributions

Life distributions with hazard rate functions of the form r(t) = Ag(t) + Bh(t) are considered. It is assumed that g(t) and h(t) are known and are independent of the unknown parameters A and B. The maximum likelihood estimators are studied for complete and censored samples. The estimation problem is reduced to a solution of one equation with one unknown parameter and it is observed that the solution is unique. The estimation procedure under the assumption of aging is also described. Some comments about the asymptotic variance-covariance matrix are given, and tests of hypo-theses are described for some cases.     

Classification of dichotomous and continuous variables with incomplete samples

This Article Considers the problem of classifiying an observation consisting of both binary and continuous variables based on two general incomplete training samples one from each of the two given populations. The location linear model adopted by krzanowski 1975 forms the basis of our inverstigation. For a given location, When the common dispersion matrix as Well as the corresponding cell probabilities for the underlying populations are known, exact distribution of the conditional maximum likelihood classification rule is derived. The overall error rate can be obtained and is based on linear cominations of independent non– Chi– Distributions. large sample result for the case where the cell probabilities are unknown is also available.     

Analysis op failure time data by burr distribution

Sometimes it is appropriate to model the survival and failure time data by a non-monotonic failure rate distribution. This may be desirable when the course of disease is such that mortality reaches a peak after some finite period and then slowly declines.In this paper we study Burr, type XII model whose failure rate exhibits the above behavior. The location of the critical points (at which the monotonicity changes) for both the failure rate and the mean residual life function (MRLF) are studied. A procedure is described for estimating these critical points. Necessary and sufficient conditions for the existence and uniqueness of the maximum likelihood estimates are provided and it is shown that the conditions provided by Wingo (1993) are not sufficient. A data set pertaining to fibre failure strengths is analyzed and the maximum likelihood estimates of the critical points are obtained.     

A family of tests for exponentiality against IFR alternatives

In this paper we consider the problem of testing exponentiality against IFR alternatives. A measure of deviation from exponentiality is developed and a class of test statistics are constructed on the basis of this measure. It is shown that the test statistic is an L-statistic. The asymptotic as well as the exact distributions of the test statistics are obtained and the test statistics are proved to be consistent. The Pitman efficiency has also been studied.     

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.