10.5937/sjm9-5520 = On robust information extraction from high-dimensional data

Kalina, Jan

doi:10.5937/sjm9-5520

Cited by 14 publications

(3 citation statements)

References 47 publications

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“…with several neurons in the output layer; cf. Kalina, 2014); neural networks namely represent a nonlinear regression methodology with a recently increasing number of applications within various management tasks. To give only a few examples, neural networks were used to improve the safety management within a chemical enterprise (Yuan et al, 2019), or to develop a cost-effective strategy for waste management (Azadi & Karimi-Jashni, 2016).…”

Section: Discussionmentioning

confidence: 99%

An application of directional quantiles to economic data with a multivariate response

Kalina¹

2020

Serb J Management

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Quantile regression represents a popular and useful methodology for modeling quantiles of a response variable based on one or more independent variables. Directional quantiles represent an available extension to the linear regression model with a multivariate response. However, we are not aware of any application of directional quantiles to real data in the literature. An illustration of directional quantiles to an economic dataset is presented in this paper, particularly a modeling of a two-dimensional response in the classical Engel's dataset on household consumption from the 19th century. The results reveal the directional quantiles to yield meaningful results. They order individual observations according to their depth, i.e. from the most central to the most outlying. We compare their result with those of a (more standard) outlier detection. On the whole, we perceive directional quantiles as a potentially useful tool for the analysis of data, if accompanied by a thorough analysis by standard tools.

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Section: Discussionmentioning

confidence: 99%

An application of directional quantiles to economic data with a multivariate response

Kalina¹

2020

Serb J Management

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“…However, the definition of mathematical models for the formation of the optimal mix is a very complex problem, usually solved by applying techniques of linear or nonlinear programming (Liu and Sherali 2000;Kumral 2003;Gaustad et al 2007;Kim et al 2008), intelligent systems such as artificial neural networks (Gui et al 2007;Yang et al 2008;Mihajlović et al 2010, Savic et al 2013Kalina 2014), and methods of multiple criteria optimization (Figueira et al 2005);Nikolić et al 2009;Chakraborty andChakraborty 2012, Stanujkić et al 2013).…”

Section: The Formation Of An Optimization Modelmentioning

confidence: 98%

Multi-Criteria Decision Support System for Optimal Blending Process in Zinc Production

Savic

Nikolić

Mihajlović

et al. 2014

Mineral Processing and Extractive Metallurgy Review

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The formation of input batch zinc concentrates is one of the key processes on which the quality and efficiency of all subsequent operations during the production of zinc cathodes directly depends. The optimization of the formation of a mixed batch aims at making a selection between the zinc concentrates that have different contents of useful and harmful components and to determine their share in the optimal mix. A multi criteria optimization model, which can be a good support for the defined problem of determining the optimal mixture of zinc concentrates, was developed in this study. For an evaluation of this model, according to four different research scenarios, the PROMETHEE=GAIA methodology was used to rank the considered zinc concentrates and determine their optimal share in the mix batch. In addition, the determination of the significance of the criteria in the optimization model was performed by application of the subjective AHP approach and the objective entropic approach. The results of this study, which were obtained during an analysis of a real case from the practice of a zinc production facility, indicate that the choice and formation of the optimal mixture of concentrates directly depends on the subjective or objective type of assessment of the significance of several chemical components in the zinc concentrates, and on the market price of the considered concentrates.

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“…The nonlinear least weighted squares (NLWS) estimator represents an extension of the least weighted squares estimator from the linear regression and at the same time a weighted analogy of the NLTS estimator [Víšek 2011;Kalina 2014]. Let us assume the magnitudes w 1 , .…”

Section: Nonlinear Least Weighted Squaresmentioning

confidence: 99%

Statistical learning for recommending (robust) nonlinear regression methods

Kalina

Tichavský

2019

Journal of Applied Mathematics, Statistics and Informatics

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We are interested in comparing the performance of various nonlinear estimators of parameters of the standard nonlinear regression model. While the standard nonlinear least squares estimator is vulnerable to the presence of outlying measurements in the data, there exist several robust alternatives. However, it is not clear which estimator should be used for a given dataset and this question remains extremely difficult (or perhaps infeasible) to be answered theoretically. Metalearning represents a computationally intensive methodology for optimal selection of algorithms (or methods) and is used here to predict the most suitable nonlinear estimator for a particular dataset. The classification rule is learned over a training database of 24 publicly available datasets. The results of the primary learning give an interesting argument in favor of the nonlinear least weighted squares estimator, which turns out to be the most suitable one for the majority of datasets. The subsequent metalearning reveals that tests of normality and heteroscedasticity play a crucial role in finding the most suitable nonlinear estimator.

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10.5937/sjm9-5520 = On robust information extraction from high-dimensional data

Cited by 14 publications

References 47 publications

An application of directional quantiles to economic data with a multivariate response

An application of directional quantiles to economic data with a multivariate response

Multi-Criteria Decision Support System for Optimal Blending Process in Zinc Production

Statistical learning for recommending (robust) nonlinear regression methods

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