ASYMPTOTIC NORMALITY OF PARAMETERSESTIMATION IN EV MODEL WITH REPLICATEDOBSERVATIONS

1 IntroductionFOrnully the EV (Errors-ill-Vaiables) nrodel is just tlie regressiOli medel with both depen-dent aud iudependeut variables arc subject to error (see, fOr exan1ple, [21~[71 alid tlie literaturecited tl1ere). Collsiderillg tliat iu mauy practical aPplications, taking replicated observationspresellts lio esseutial dimculties. heuce can offer a couveuiellt choice in avoidillg artilicia1 as-sunWtions about tlie nlodel. This case was studied ill essay [11, in which estinators of a …     

Estimation of Parameters of Partially Linear Errors-in-variables Models with Replicated Net Points of Observation

A kind of partially linear errors-in-variables models with replicated net points of observation are studied in this paper. Estimators of unknown parameters are given. Under certain regular conditions, it is shown that the estimators of the unknown parameters are strongly consistent and their a.s. convergence rates are achieved.     

Consistent estimation and testing in heteroscedastic polynomial errors-in-variables models

The paper concentrates on consistent estimation and testing in functional polynomial measurement errors models with known heterogeneous variances. We rest on the corrected score methodology which allows the derivation of consistent and asymptotically normal estimators for line parameters and also consistent estimators for the asymptotic covariance matrix. Hence, Wald and score type statistics can be proposed for testing the hypothesis of a reduced linear relationship, for example, with asymptotic chi-square distribution which guarantees correct asymptotic significance levels. Results of small scale simulation studies are reported to illustrate the agreement between theoretical and empirical distributions of the test statistics studied. An application to a real data set is also presented.     

Functional relationships having many independent variables and errors with multivariate normal distribution

This paper deals with maximum likelihood estimation of linear or nonlinear functional relationships assuming that replicated observations have been made on p variables at n points. The joint distribution of the pn errors is assumed to be multivariate normal. Existing results are extended in two ways: first, from known to unknown error covariance matrix; second, from the two variate to the multivariate case.For the linear relationship it is shown that the maximum likelihood point estimates are those obtained by the method of generalized least squares. The present method, however, has the advantage of supplying estimates of the asymptotic covariances of the structural parameter estimates.     

有重复观测时二次EV模型参数的强相合性

给出了在一些实验点重复观测时二次EV模型参数的估计，在一般条件下证明了估计是强相合的。     

On nonlinear weighted errors-in-variables parameter estimation problem in the three-parameter Weibull model

This paper is concerned with the three-parameter Weibull distribution which is widely used as a model in reliability and lifetime studies. In practice, the Weibull model parameters are not known in advance and must be estimated from a random sample. Difficulties in applying the method of maximum likelihood to three-parameter Weibull models have led to a variety of alternative approaches in the literature. In this paper we consider the nonlinear weighted errors-in-variables (EIV) fitting approach. As a main result, two theorems on the existence of the EIV estimate are obtained. An illustrative example is also included.