IFS approximations of distribution functions and related optimization problems.
Improving on Two-Stage Estimators for Scale Families.
In Information Theoretic Argument for the Validity of the Exponential Model.
Inference on overlap coefficients under the Weibull distribution : equal shape parameter
In this paper we consider three measures of overlap, namely Matusia’s measure , Morisita’s measure and Weitzman’s measure . These measures are usually used in quantitative ecology and stress-strength models of reliability analysis. Herein we consider two Weibull distributions having the same shape parameter and different scale parameters. This distribution is known to be the most flexible life distribution model with two parameters. Monte Carlo evaluations are used to study the bias and precision...
Inference on overlap coefficients under the Weibull distribution: Equal shape parameter
In this paper we consider three measures of overlap, namely Matusia's measure ρ, Morisita's measure λ and Weitzman's measure Δ. These measures are usually used in quantitative ecology and stress-strength models of reliability analysis. Herein we consider two Weibull distributions having the same shape parameter and different scale parameters. This distribution is known to be the most flexible life distribution model with two parameters. Monte Carlo evaluations are used to study the bias and precision...
Inference on the location parameter of exponential populations
Studentization and analysis of variance are simple in Gaussian families because X̅ and S² are independent random variables. We exploit the independence of the spacings in exponential populations with location λ and scale δ to develop simple ways of dealing with inference on the location parameter, namely by developing an analysis of scale in the homocedastic independent k-sample problem.
Inferencia bayesiana en mixturas: métodos aproximados.
The problem of approximating mixtures of distributions has received considerable attention recently. In this paper we consider problems of estimating the mixing proportions of a finite mixture from a Bayesian perspective. The problems which arise from this methodology are basically approximations of finite measures of distributions. We propose two approximating methods and prove that under certain conditions both methods are asymptotically equivalent to a third method, which turns out to be simpler...
Inferential procedures on a generalized Rayleigh variate. I
Inferential procedures on a generalized Rayleigh variate. II
Information inequalities for the minimax risk of sequential estimators (with applications)
Information inequalities for the minimax risk of sequential estimators are derived in the case where the loss is measured by the squared error of estimation plus a linear functional of the number of observations. The results are applied to construct minimax sequential estimators of: the failure rate in an exponential model with censored data, the expected proportion of uncensored observations in the proportional hazards model, the odds ratio in a binomial distribution and the expectation of exponential...
Insensitivity region for variance components in general linear model
In linear regression models the estimator of variance components needs a suitable choice of a starting point for an iterative procedure for a determination of the estimate. The aim of this paper is to find a criterion for a decision whether a linear regression model enables to determine the estimate reasonably and whether it is possible to do so when using the given data.
Inverse Cauchy problem and unbiased estimation
Least squares for practitioners.
Likelihood and quasi - likelihood estimation of transition probabilities
In the paper two approaches to the problem of estimation of transition probabilities are considered. The approach by McCullagh and Nelder [5], based on the independent model and the quasi-likelihood function, is compared with the approach based on the marginal model and the standard likelihood function. The estimates following from these two approaches are illustrated on a simple example which was used by McCullagh and Nelder.
Likelihood for random-effect models (with discussion).
For inferences from random-effect models Lee and Nelder (1996) proposed to use hierarchical likelihood (h-likelihood). It allows influence from models that may include both fixed and random parameters. Because of the presence of unobserved random variables h-likelihood is not a likelihood in the Fisherian sense. The Fisher likelihood framework has advantages such as generality of application, statistical and computational efficiency. We introduce an extended likelihood framework and discuss why...
Linear comparative calibration with correlated measurements
The paper deals with the linear comparative calibration problem, i. e. the situation when both variables are subject to errors. Considered is a quite general model which allows to include possibly correlated data (measurements). From statistical point of view the model could be represented by the linear errors-in-variables (EIV) model. We suggest an iterative algorithm for estimation the parameters of the analysis function (inverse of the calibration line) and we solve the problem of deriving the...
Linear error propagation law and nonlinear functions
Linear error propagation law (LEPL) has been using frequently also for nonlinear functions. It can be adequate for an actual situation however it need not be so. It is useful to use some rule in order to recognize whether LEPL is admissible. The aim of the paper is to find such rule.
Linear statistical models with constraints revisited
Linear versus quadratic estimators in linearized models
In nonlinear regression models an approximate value of an unknown parameter is frequently at our disposal. Then the linearization of the model is used and a linear estimate of the parameter can be calculated. Some criteria how to recognize whether a linearization is possible are developed. In the case that they are not satisfied, it is necessary to take into account either some quadratic corrections or to use the nonlinear least squares method. The aim of the paper is to find some criteria for an...
Linearization conditions for regression models with unknown variance parameter
In the case of the nonlinear regression model, methods and procedures have been developed to obtain estimates of the parameters. These methods are much more complicated than the procedures used if the model considered is linear. Moreover, unlike the linear case, the properties of the resulting estimators are unknown and usually depend on the true values of the estimated parameters. It is sometimes possible to approximate the nonlinear model by a linear one and use the much more developed linear...