Theoretical notions of statistical estimation

Constantin Anghelache, Madalina-Gabriela Anghel, Ihab Jweida S J Jweida, Marius Popovici and Emilia Stanciu
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Constantin Anghelache: Academia de Studii Economice din Bucuresti/Universitatea „Artifex„ din Bucuresti
Madalina-Gabriela Anghel: Universitatea „Artifex„ din Bucuresti
Ihab Jweida S J Jweida: Academia de Studii Economice din Bucuresti
Marius Popovici: Academia de Studii Economice din Bucuresti
Emilia Stanciu: Academia de Studii Economice din Bucuresti

Romanian Statistical Review Supplement, 2016, vol. 64, issue 11, 120-126

Abstract: This article will address traditional assessment methods, such as maximum likelihood, useful when it is known. Conversely, it is not known, we can use nonparametric methods exploiting specific property that require the involvement of a distribution functions. Models with discrete variables and partially observed models are usually estimated by maximum likelihood method. We will address some models presented in the first section of this chapter and will use their traditional presentation as a model of parametric indices. We will address the theory of regression observation asymptotically unbiased estimator using positive and analyzing their effectiveness. We will further analyze the types of errors that are generated from regression and heteroscedastic.

Keywords: traditional methods of estimation; distribution functions; parametric indices; logarithmic probabilities; binary selection model (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:rsr:supplm:v:64:y:2016:i:11:p:120-126

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