The errors ei in (1.1) are assumed to be independent and identically distributed, but are not necessarily normal and may be heavy-tailed. Assume for convenience that β is one dimensional. Then (1.1) is a simple linear regression. However, most of the following extends more-or-less easily to higher-dimensional β, in which case (1.1) is a multiple regression. Given β, define Ri(β) as the rank (or...

Recently, in this journal, there has been revised attention on estimating the parameters of the errors in variables, linear structural model. For example, O’Driscoll and Ramirez (2011) used a geometric approach to give insight into the performance of various slope estimators for the linear structural model as introduced by the present author. This paper aims to provide a unified method of momen...

A method is proposed which simplifies estimation of all parameters in the functional measurement error model with no error in the equation under the assumption that the error covariance for the explanatory variables is known and the model variance is unknown. The proposed method is shown to produce the same estimates given for the structural model in Schneeweiss (1976) and Fuller(1987) .

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The pap...

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The pap...

R. DEVILLERS*{{, Y. BÉDARD{, R. JEANSOULIN{§ and B. MOULIN{ {Department of Geography, Memorial University of Newfoundland, St. John’s (NL) A1B 3X9, Canada {Centre de Recherche en Géomatique (CRG), Pavillon Casault, Université Laval, Québec (QC), G1K 7P4, Canada §Laboratoire des Sciences de l’Information et des Systèmes (LSIS), Centre de Mathématiques et d’Informatique (CMI), Université de Prove...

We consider the nonparametric estimation of the regression functions for dependent data. Suppose that the covariates are observed with additive errors in the data and we employ nonparametric deconvolution kernel techniques to estimate the regression functions in this paper. We investigate how the strength of time dependence affects the asymptotic properties of the local constant and linear esti...

The paper is a survey of recent investigations by the authors and others into the relative efficiencies of structural and functional estimators of the regression parameters in a measurement error model. While structural methods, in particular the quasi-score (QS) method, take advantage of the knowledge of the regressor distribution (if available), functional methods, in particular the corrected...

Climate change is one of the challenges which affect different areas of the human’s life on the earth, one of which is its effect on extreme events such as flood and drought. Today, one of the most important topics being discussed is that how the intensity of heavy precipitations and flood will change in future compared to that of the present values. In the present study, we try to investigate ...

Background Micro-RNAs (miRNAs) are a class of small non-coding RNA molecules that function though post transcriptional regulation of gene expression by a process termed RNA interference (RNAi). RNAi–mediated targeting of viral RNAs is recognized as an antiviral defense mechanism. The epigenetic effect of miRNAs can either be direct, by interfering with virus genome, or indirect, through downreg...