Generalized Cramér-von Mises tests of goodness of fit for doubly censored data

We generalize Cramér-von Mises statistics to test the goodness of fit of a lifetime distribution when the data are doubly censored. We derive the limiting distributions of our test statistics under the null hypothesis and the alternative hypothesis, respectively. We also give a strong consistent estimator for the asymptotic covariance of the self-consistent estimator for the survival function with doubly censored data. Thereby, a method, called the Fredholm Integral Equation method, is proposed to estimate the null distribution of test statistics. In this work, the perturbation theory for linear operators plays an important role, and some numerical examples are included.The author's research was supported by a Faculty Fellowship of University of Nebraska-Lincoln.     

Estimations from the exponentiated rayleigh distribution based on generalized Type-II hybrid censored data

In this article, unknown parameters of exponentiated Rayleigh distribution based on generalized Type II Hybrid censored data, survival function, failure rate function and coefficient of variation are derived by applying the maximum likelihood, Bayes and percentile bootstrap methods. Approximate confidence intervals for the unknown parameters, survival function, failure rate function and coefficient of variation are obtained. We study Bayes estimates under gamma priors distributions depending on symmetric and asymmetric loss functions via the Gibbs within Metropolis-Hasting samplers procedure. Finally, the proposed methods can be understood through illustrating the results of the real data analysis.     

The asymptotic distribution of a goodness of fit statistic for factorial invariance

Suppose that random factor models with k factors are assumed to hold for m, p-variate populations. A model for factorial invariance has been proposed wherein the covariance or correlation matrices can be written as Σ_i = LC_iL′ + σ_i²I, where C_i is the covariance matrix of factor variables and L is a common factor loading matrix, i = 1,…, m. Also a goodness of fit statistic has been proposed for this model. The asymptotic distribution of this statistic is shown to be that of a quadratic form in normal variables. An approximation to this distribution is given and thus a test for goodness of fit is derived. The problem of dimension is considered and a numerical example is given to illustrate the results.     

Fitting the lognormal distribution to censored labour wastage data

In this paper an estimation technique is developed on the basis of the well-known lognormal hypothesis for labour wastage to estimate the early leavers for each entry cohort. Early leavers are those leavers who joined and left the company during the twelve-month period and are not recorded at the census data taken at the end of this period. The results of applying the lognormal model on the extended data in predicting survivors for several companies are then shown to be improved.     

Testing goodness of fit for the distribution of errors in multivariate linear models

In this paper, to test goodness of fit to any fixed distribution of errors in multivariate linear models, we consider a weighted integral of the squared modulus of the difference between the empirical characteristic function of the residuals and the characteristic function under the null hypothesis. We study the limiting behaviour of this test statistic under the null hypothesis and under alternatives. In the asymptotics, the rank of the design matrix is allowed to grow with the sample size.     

GENERALIZED PARETO DISTRIBUTION FIT TO MEDICAL INSURANCE CLAIMS DATA

How to choose an optimal threshold is a key problem in the generalized Pareto distribution （GPD） model. This paper attains the exact threshold by testing for GPD,and shows that GPD model allows the actuary to easily estimate high quantiles and the probable maximum loss from the medical insurance claims data.     

Modified Kent's statistics for testing goodness of fit for the Fisher distribution in small concentrated samples

The limiting null distribution of Kent's (1982) statistic, to test whether a sample comes from the Fisher distribution is derived when κ, the concentration parameter, goes to ∞. A modification is suggested, the limiting null distribution of which is ξ₂² when either κ or n, the sample size, goes to ∞. Tests of Fisherness based on the eigenvalues of the sample cross product matrix are also considered. Numerical examples are presented.     

Evaluating the goodness of fit of stochastic mortality models

This study sets out a framework to evaluate the goodness of fit of stochastic mortality models and applies it to six different models estimated using English & Welsh male mortality data over ages 64-89 and years 1961-2007. The methodology exploits the structure of each model to obtain various residual series that are predicted to be iid standard normal under the null hypothesis of model adequacy. Goodness of fit can then be assessed using conventional tests of the predictions of iid standard normality. The models considered are: Lee and Carter’s (1992) one-factor model, a version of Renshaw and Haberman’s (2006) extension of the Lee-Carter model to allow for a cohort-effect, the age-period-cohort model, which is a simplified version of the Renshaw-Haberman model, the 2006 Cairns-Blake-Dowd two-factor model and two generalized versions of the latter that allow for a cohort-effect. For the data set considered, there are some notable differences amongst the different models, but none of the models performs well in all tests and no model clearly dominates the others.     

Bahadur efficiencies of spacings tests for goodness of fit

This paper is concerned with the exact Bahadur efficiencies of spacings statistics. For a general class of statistics based on a fixed number of spacings, the explicit forms of the exact slopes are derived, and it is shown that the sum of the logarithms of spacings is optimal in this class. Some results are extended to the case where the number of spacings increase with the sample size to infinity.     

Non-parametric estimation of the generalized past entropy function with censored dependent data

The generalized past entropy function introduced by Gupta and Nanda (2002) is viewed as a dynamic measure of uncertainty in past life. This measure finds applications in modeling past life time data. In the present work we provide non-parametric kernel-type estimator for the generalized past entropy function based on censored data. Asymptotic properties of the estimator are established under suitable regularity conditions. Simulation studies are carried out using the Monte Carlo method.     

N-distance tests for a composite hypothesis of goodness of fit

In this paper, we consider an application of N-distance theory for testing composite hypotheses of goodness of fit. This work is a continuation of our research started in [A. Bakshaev, Goodness of fit and homogeneity tests on the basis of N-distances, J. Stat. Plan. Inference, 139(11):3750–3758, 2009; A. Bakshaev, Nonparametric tests based on N-distances, Lith. Math. J., 48(4):368–379, 2008]. Particular attention is paid to normality and exponentiality tests. A comparative Monte Carlo power study for the proposed criteria is provided. Different alternatives in uni- and bivariate cases are investigated.     

A new generalized linear exponential distribution and its applications

A new generalized linear exponential distribution （NCLED） is considered in this paper which can be deemed as a new and more flexible extension of linear exponential distribution. Some statistical properties for the NGLED such as the hazard rate function, moments, quantiles are given. The maximum likelihood estimations （MLE） of unknown parameters are also discussed. A simulation study and two real data analyzes are carried out to illustrate that the new distribution is more flexible and effective than other popular distributions in modeling lifetime data.     

On an analog of empirical distribution for multivariate censored data

A model of a “censored” experiment is formally defined. A functional is proposed which can be considered as an analog of the empirical distribution. Some properties of this functional are investigated. Translated fromStatisticheskie Metody Otsenivaniya i Proverki Gipotez, pp. 41–51, Perm, 1993.     

Parameter estimation of inverse Lindley distribution for Type-I censored data

In life testing experiments, Type-I censoring scheme has been widely used due to its simplicity and poise with considerable gain in the completion time of an experiment. This article deals with the parameter estimation of inverse Lindley distribution when the data is Type-I censored. Estimates have been obtained under both the classical and Bayesian paradigm. In the classical scenario, estimates based on maximum likelihood and maximum product of spacings coupled with their 95% asymptotic confidence interval have been obtained. Under the Bayesian set up, the point estimate is obtained by considering squared error loss function using Markov Chain Monte Carlo technique and highest posterior density intervals based on these samples are reckoned. The performance of above mentioned techniques are evaluated on the basis of their simulated risks. Further, a real data set is analysed for appraisal of aforementioned estimation techniques under the specified censoring scheme.     

A Wald-type variance estimation for the nonparametric distribution estimators for doubly censored data

We discuss the variance estimation for the nonparametric distribution estimator for doubly censored data. We first provide another view of Kuhn–Tucker’s conditions to construct the profile likelihood, and lead a Newton–Raphson algorithm as an optimization technique unlike the EM algorithm. The main proposal is an iteration-free Wald-type variance estimate based on the chain rule of differentiating conditions to construct the profile likelihood, which generalizes the variance formula in only right- or left-censored data. In this estimation procedure, we overcome some difficulties caused in directly applying Turnbull’s formula to large samples and avoid a load with computationally heavy iterations, such as solving the Fredholm equations, computing the profile likelihood ratio or using the bootstrap. Also, we establish the consistency of the formulated Wald-type variance estimator. In addition, simulation studies are performed to investigate the properties of the Wald-type variance estimates in finite samples in comparison with those from the profile likelihood ratio.     

Admissibility of goodness of fit tests for discrete exponential families

Consider testing the composite hypothesis that a population is a particular discrete exponential family. For example, a Poisson distribution with unknown parameter. We find a sufficient condition for the admissibility of goodness of fit tests. Included in the class of admissible tests is the usual chi — square test of goodness of fit.     

A collocation method to fit geophysical survey data

A self adaptive collocation method has been applied to the quantitative interpretation of geophysical survey data. Examples of interpretation of vertical electrical soundings (VES) and magneto-telluric soundings (MTS) have been chosen for this work due to the high nonlinearity of the response functions and to the high degree of correlation of the parameters involved. In both cases horizontally isotropic multi-layered models have been adopted.For comparison two different computing algorithms have been considered. In the first, using a generalized inverse formulation a new different decomposition into characteristic values has been used. In the second the Marquardt procedure has been modified in an original way to give a faster convergence using a strategy based on the contemporary adjustment of the step length and the direction of the regression. The confidence limits of the results have been evaluated.Results are given for interpretation of both MTS and VES curves using the two different algorithms. Model data and field data have been considered for an effective comparison of speed of convergence and accuracy.     

Bootstrap for the conditional distribution function with truncated and censored data

We propose a resampling method for left truncated and right censored data with covariables to obtain a bootstrap version of the conditional distribution function estimator. We derive an almost sure representation for this bootstrapped estimator and, as a consequence, the consistency of the bootstrap is obtained. This bootstrap approximation represents an alternative to the normal asymptotic distribution and avoids the estimation of the complicated mean and variance parameters of the latter.     

A sequential test for the failure rate of an exponential distribution with censored data

This paper provides an optimal sequential decision procedure for deciding between two composite hypotheses about the unknown failure rate of an exponential distribution, using censored data. The procedure has two components, a stopping time and a decision function. The optimal stopping time minimizes the expected total loss due to a wrong decision plus cost of observing the process. The optimal decision function is easily characterized once a stopping time has been specified. The main result determines the continuation region for the optimal decision procedure