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Displaying similar documents to “Exploring the impact of post-training rounding in regression models”

Detecting atypical data in air pollution studies by using shorth intervals for regression

Cécile Durot, Karelle Thiébot (2010)

ESAIM: Probability and Statistics

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To validate pollution data, subject-matter experts in Airpl (an organization that maintains a network of air pollution monitoring stations in western France) daily perform visual examinations of the data and check their consistency. In this paper, we describe these visual examinations and propose a formalization for this problem. The examinations consist in comparisons of so-called shorth intervals so we build a statistical test that compares such intervals in a nonparametric regression...

Note on universal algorithms for learning theory

Karol Dziedziul, Barbara Wolnik (2007)

Applicationes Mathematicae

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We study the universal estimator for the regression problem in learning theory considered by Binev et al. This new approach allows us to improve their results.

Directional quantile regression in Octave (and MATLAB)

Pavel Boček, Miroslav Šiman (2016)

Kybernetika

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Although many words have been written about two recent directional (regression) quantile concepts, their applications, and the algorithms for computing associated (regression) quantile regions, their software implementation is still not widely available, which, of course, severely hinders the dissemination of both methods. Wanting to partly fill in the gap here, we provide all the codes needed for computing and plotting the multivariate (regression) quantile regions in Octave and MATLAB,...

Selection in parametric models via some stepdown procedures

Konrad Furmańczyk (2014)

Applicationes Mathematicae

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The paper considers the problem of consistent variable selection in parametic models with the use of stepdown multiple hypothesis procedures. Our approach completes the results of Bunea et al. [J. Statist. Plann. Inference 136 (2006)]. A simulation study supports the results obtained.

On inconsistency of Hellwig's variable choice method in regression models

Tadeusz Bednarski, Filip Borowicz (2009)

Discussiones Mathematicae Probability and Statistics

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It is shown that a popular variable choice method of Hellwig, which is recommended in the Polish econometric textbooks does not enjoy a very basic consistency property. It means in particular that the method may lead to rejection of significant variables in econometric modeling. A simulation study and a real data analysis case are given to support theoretical results.

Stacked regression with restrictions

Tomasz Górecki (2005)

Discussiones Mathematicae Probability and Statistics

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When we apply stacked regression to classification we need only discriminant indices which can be negative. In many situations, we want these indices to be positive, e.g., if we want to use them to count posterior probabilities, when we want to use stacked regression to combining classification. In such situation, we have to use leastsquares regression under the constraint βₖ ≥ 0, k = 1,2,...,K. In their earlier work [5], LeBlanc and Tibshirani used an algorithm given in [4]. However,...

How to deal with regression models with a weak nonlinearity

Eva Tesaríková, Lubomír Kubáček (2001)

Discussiones Mathematicae Probability and Statistics

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If a nonlinear regression model is linearized in a non-sufficient small neighbourhood of the actual parameter, then all statistical inferences may be deteriorated. Some criteria how to recognize this are already developed. The aim of the paper is to demonstrate the behaviour of the program for utilization of these criteria.

Seasonal forecasting of tropical cyclone activity in the Australian and the South Pacific Ocean regions

J.S. Wijnands, G. Qian, K.L. Shelton, R.J.B. Fawcett, J.C.L. Chan, Y. Kuleshov (2015)

Mathematics of Climate and Weather Forecasting

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The Australian Bureau of Meteorology (Bureau) issues operational tropical cyclone (TC) seasonal forecasts for the Australian region (AR) and the South Pacific Ocean (SPO) and subregions therein. The forecasts are issued in October, ahead of the Southern Hemisphere TC season (November to April). Improvement of operational TC seasonal forecasts can lead to more accurate warnings for coastal communities to prepare for TC hazards. This study investigates the use of support vector regression...