Nonparametric estimation of the hazard function under dependence conditions

The estimation of the hazard rate has a great number of practical appli¬cations in dependence situations (seismicity analysis, reliability, economics), Based on kernel estimates of the density and the distribution function, we study the properties of the nonparametric estimator of the hazard function as-sociated with a strongly mixing time series. We prove consistency and asymp¬totic normality properties, and a cross-validation method for the smoothing parameter selection is studied. Some simulations and a practical application to real data are also shown.     

Parameter estimation based upon nonparametric function estimators

By considering the solution to a linear approximation of a nonlinear regression problem, a procedure for developing a para¬meter estimator, based upon a nonpammetric estimator of a para¬metric function, is given. The resulting estimators, which are determinable in closed form, are asymptotically normally distri¬buted and are optimal among the class of estimators based upon the function estimator. Further, in many cases, the estimator will have the same asymptotic distribution theory as the correspond¬ing maximum likelihood estimator. Estimators based upon the Kaplan-Meier quantile function are developed for randomly censored samples.     

Estimation of the parameters of a regression model with a multivariate t error variable

This paper deals with a regression model for several vari¬ables under the assumption that the errors have a multivariate t-distribution. The parameters of the model, the regression parameters, as well as the scale parameters and the degress of freedom of the error variable are estimated and the estimation procedure is illustrated by a numerical example, Also, the prop¬erties of the estimators and tests for the regression parameters are discussed.     

A class of semiparametric transormation frailty models with application to estimating treatment effects and heterogenity in aids clinical traials

Muitivariate failure time data are common in medical research; com¬monly used statistical models for such correlated failure-time data include frailty and marginal models. Both types of models most often assume pro¬portional hazards (Cox, 1972); but the Cox model may not fit the data well This article presents a class of linear transformation frailty models that in¬cludes, as a special case, the proportional hazards model with frailty. We then propose approximate procedures to derive the best linear unbiased es¬timates and predictors of the regression parameters and frailties. We apply the proposed methods to analyze results of a clinical trial of different dose levels of didansine (ddl) among HIV-infected patients who were intolerant of zidovudine (ZDV). These methods yield estimates of treatment effects and of frailties corresponding to patient groups defined by clinical history prior to entry into the trial.     

Randomization and blocking in the design of experiments

It is suggested that, whenever possible, an experiment be run in a completely randomized fashion. One reason for randomizing Is to protect against violations in the usual linear model assump¬tions. The protection has always been argued on qualitative grounds. This paper quantitatively demonstrates the protection by hypothesizing models in violation of the usual assumptions, mathe¬matically representing the physical act of randomization, and algebraically deriving expected mean squares, EMS, and F tests. It is shown that randomization offers considerable but not com¬plete protection against model violations.

The same methodology is also applied to blocked experiments, i.e. to experiments performed under a specific type of incomplete randomization commonly referred to as blocking. It is shown that blocking offers little protection against certain model viola¬tions. The common practice of representing blocks as a treatment factor applied to the experimental units approximates the form of the EMS derived under the violated assumptions model.     

The beta exponentiated Weibull distribution

The Weibull distribution is one of the most important distributions in reliability. For the first time, we introduce the beta exponentiated Weibull distribution which extends recent models by Lee et al. [Beta-Weibull distribution: some properties and applications to censored data, J. Mod. Appl. Statist. Meth. 6 (2007), pp. 173–186] and Barreto-Souza et al. [The beta generalized exponential distribution, J. Statist. Comput. Simul. 80 (2010), pp. 159–172]. The new distribution is an important competitive model to the Weibull, exponentiated exponential, exponentiated Weibull, beta exponential and beta Weibull distributions since it contains all these models as special cases. We demonstrate that the density of the new distribution can be expressed as a linear combination of Weibull densities. We provide the moments and two closed-form expressions for the moment-generating function. Explicit expressions are derived for the mean deviations, Bonferroni and Lorenz curves, reliability and entropies. The density of the order statistics can also be expressed as a linear combination of Weibull densities. We obtain the moments of the order statistics. The expected information matrix is derived. We define a log-beta exponentiated Weibull regression model to analyse censored data. The estimation of the parameters is approached by the method of maximum likelihood. The usefulness of the new distribution to analyse positive data is illustrated in two real data sets.     

Estimation of parameters of the gped

Janardan (1973) introduced the generalized Polya-Eggenberger distribution as a limiting form of the generalized Markov-Polya distribution (GMPD), Ja¬nardan (1998) derived GPED formally by means of Lagrange's expansion and discussed its various properties systematically. Here, a new urn model is pro¬vided for the GPED. Moment estimators of the parameters are given in closed form. Maximum hkelihood estimators are also given. Some apphcations are provided.     

An evaluation of the huber estimator

The asymptotic efficiency for the Huber estimator is evalu¬ated for distributions in the class of normal scale mixtures, gamma power, and Student's t-distributions. Rather high efficien¬cies are found for small deviations from normality, while intract¬able efficiencies occur for larger deviations.     

Multiyear,through the season crop acreage estimation using estimated acreage in sample segments

Large scale crop surveys can be made frequently and inex¬pensively during a crop growing season using Landsat data. A crop's at-harvest acreage in a stratum can be estimated from the crop's estimated at-harvest acreage in a small sample of the stratum's segments. The stratum estimate can utilize Landsat imagery obtained during the current crop grow¬ing season and in previous years. A mixed effects analysis of variance model is used to generate a weighted least squares es¬timate of the stratum at-harvest acreage proportion for the cur¬rent year. Similar Landsat based stratum crop proportion esti¬mates can be combined with historical information on non-sampled (or unsuccessfully sampled) strata to provide crop acreage estimates for large regions. These regional estimates of the at-harvest acreage can be determined early in the crop growing season, at different intermediate points, and at har¬vest time     

Regular mles for nonregular distributions

Distributions whose extremity values of the support depend on unknown pa¬rameters are usually known as nonregular distributions. In most cases, the MLEs for these parameters cannot be obtained by differentiation. Familiar examples are the uniform distribution on the interval (0,0) and the truncated exponential distribution with truncation parameter 0. However, there exist distributions whose extremity points of the support depend on unknown pa¬rameters, which nevertheless are regular in the sense that the MLEs can be obtained by differentiation. This note provides a method of constructing such nonregular distributions with regular MLEs.     

A note on bootstrap and other empirical procedures for testing linear hypotheses ’without normality

For testing linear hypotheses without the assumption of normal distributions besides the asymptotically optimal test there exists a lot of possibilities to construct em¬pirical procedures by approximating the unknown distribution of the test statistic or some of its parameters from the sample itself. This note contains comparisons of some of such procedures for small samples. In the easiest case of an unknown, mean in a small simulation study there the actual significance level is compared with the nominal level a. Two empiri¬cal procedures seem to be as good as the asymptotical procedure while the others, including a bootstrap procedure, seem to be unreliable     

Distribution-free partially sequential piacment procedures

The concept of a partially sequential hypothesis test was introduced by Wolfe (1977a), an{associated procedures were developed for both parametric and nonparametric assumptions. In this paper we consider distribution-free extensions of those indicator tests, based on the placements of the sequentially obtained observations among the previously collected fixed size sample. Exact and asymptotic, as the fixed sample size in¬creases to infinity, properties of these sequential placements procedures are obtained, including statements about the power and expected number of sequentially obtained observations. The results of a Monte Carlo study are used to differentiate be¬tween various placement scoring schemes.     

Poisson bounds for moments of shot noise processes

Starting from general moment formulas for shot noise processes (v(t)), for which a simple proof is given by means of the CAMPBELL theorem concerning marked point pro¬cesses, and using certain monotonicity property, bounds are obtained for E[v(t)]² resp. for the variance Var v(t) under the additional assumption that the intershot times are inde¬pendent random variables with distribution having the property 1SFBXJE (NWTJE)     

For censored and truncated data

In this paper we develop nonparametric methods for regression analysis when the response variable is subject to censoring and/or truncation. The development is based on a data completion princple that enables us to apply, via an iterative scheme, nonparametric regression techniques to iteratively com¬pleted data from a given sample with censored and/or truncated observations. In particular, locally weighted regression smoothers and additive regression models are extended to left-truncated and right-censored data Nonparamet¬ric regression analysis is applied to the Stanford heart transplant data, which have been analyzed by previous authors using semiparametric regression meth¬ods. and provides new insights into the relationship between expected survival time after a heart transplant and explanatory variables.     

Comparative performance of tests of normality in detecting mixtures of parallel regression lines

This paper addresses the problem of detecting a mixture of parallel regression lines when information about group member¬ship of individual cases is not given. The problem is approached as a missing variable problem, with the missing variables being the dummy variables that code for groups. If a mixture of par¬allel regression lines with normally distributed error terms is present, a simple regression model without dummy variables will produce residuals that follow approximately a mixed normal dis¬tribution. In a simulation studyr several goodness-of-fit tests of normality were used to test the residuals obtained from mis-specified models that excluded dummy variables, Factors varied in the simulation included the number and the separation of the parallel lines and the sample size, The goodness-of-fit test based on the sample kurtosis (82) was overall most powerful in detecting mixtures of parallel regression lines, Applications are discussed.     

On the asymptotic behaviour of selector statistics

Adaptive procedures proposed by Hogg are based on selector statistics for the skewness and the tails. The asymptotic properties of several proposed selector statistics are investigated. Since ail these statistics have under some assumptions asymptoti¬cally a normal distribution, their properties depend on the asymptotic bias and variance. The reasonable concept to compare the different selector statistics is based on the selection probabil¬ities in discriminating the type of the underlying distribution. These values are numerically calculated and analyzed in detail for a number of underlying distributions.     

Ridge estimator in singulah oesiun with application to age-period-cohort analysis of disease rates

Ridge estimator of a singular design is considered for linear and gener¬alized linear models. Ridge penalty helps determine a unique estimator in singmar uesign. me tuning parameter o± tue penalty is seiecteu via gener¬alized cross-validation (GCV) method. It is proven that the ridge estimator lies in a special sub-parameter space and converges to the intrinsic estimator, an estimable function in singular design, as the shrinkage penalty diminishes. The expansion of the ridge estimator and its variance are also obtained. Thismethod is demonstrated through an application to age-period-cohort (APC) analysis of the incidence rates of cervical cancer in Ontario women 1980-1994     

Recurrence relations of coefficients of the generalized hypergeometric function in multivariate analysis

James(1960) defined the zonal polynomials and used it to represent the joint distributions of latent roots of VVisfiart matrix. The zonal polviionnals played an important role to define the generalized hypergeometric function of symmetric matrix argument Since then, many density functions and moments based on Wishart matrix have been expressed in terms of the generalized hy¬pergeometric Function. The purpose of this paper is to get the recurrence relations for the coefficients of it. In Section 1 we derive a partial differen¬tial equations having the generalized hypergeometric function as the unique solution. Then we ubtain the recurrence relations until order 7 in Section 2.     

Hypothesis tests for linear functional relationships

The analysis of linear functional relationships is considered. Expressions useful for the estimation of both unconstrained and con¬strained parameters are presented. The testing of hypotheses which consist of linear constraints on the parameters is discussed. Wald type and Wilks type test statistics and their asymptotic null dis¬tributions are derived. It appears that these test statistics are not asymptotically equivalent.     

A class of closeness criteria

A generalized class of closeness criteria for the pairwise comparison of esti¬mators is defined. This class contains an infinite number of members including Pitman's measure of closeness and at least one transitive criterion. Several specific members of the class are examined, and their relationships to Rao concentration and stochastic domination are shown. Graphical and analyti¬cal characterizations are shown for these members of the class. Examples are given which illustrate the behavior of some of these criteria.