An algorithm for conditional multidimensional parameter identification with asymmetric and correlated losses of under- and overestimations. Issue 5 (23rd March 2016)

• Record Type: Journal Article
• Authors: Kulczycki, Piotr; Charytanowicz, Malgorzata
• Is Part Of: Journal of statistical computation and simulation. Volume 86:Issue 5(2016)
• Page Start: 1032
• Page End: 1055
• Publication Date: 2016-03-23
• Subjects: identification of vector of parameters -- conditioning factors -- Bayes approach -- asymmetrical loss function -- nonparametric estimation -- distribution free method -- numerical algorithm; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2015.1049606
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : Many of today's specialized applicational tasks are obliged to consider the influence of inevitable errors in the identification of parameters appearing in a model. Favourable results can also be achieved through measuring, and then accounting for definite (e.g. current) values of factors which show a significant reaction to the values of those parameters. This paper is dedicated to the problem of the estimation of a vector of parameters, where losses resulting from their under- and overestimation are asymmetric and mutually correlated. The issue is considered from a supplementary conditional aspect, where particular coordinates of conditioning variables may be continuous, discrete, multivalued (in particular binary) or categorized (ordered and unordered). The final result is a ready-to-use algorithm for calculating the value of an estimator, optimal in the sense of minimum expectation of losses using a multidimensional asymmetric quadratic function, for practically any distributions of describing and conditioning variables.