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Two procedures for robust monitoring of probability distributions of economic data stream induced by depth functions

100%

KOSIOROWSKI D.

tom 25

nr 1

55-79

Data streams (streaming data) consist of transiently observed, evolving in time, multidimensional data sequences that challenge our computational and/or inferential capabilities. We propose user friendly approaches for robust monitoring of selected properties of unconditional and conditional distributions of the stream based on depth functions. Our proposals are robust to a small fraction of outliers and/or inliers, but at the same time are sensitive to a regime change in the stream. Their implementations are available in our free R package DepthProc

Robustness of Depth Based Classification Rules

100%

Kosiorowski D. , Department of Statistics C. U. o. E.

Acta Universitatis Lodziensis. Folia Oeconomica

2009

tom 228

W referacie proponujemy kilka reguł klasyfikacyjnych wykorzystujących funkcje głębi. Badamy ich właściwości m. in. na różnych zbiorach danych generowanych przez wielowymiarowe skośne rozkłady, rozkłady o tłustych ogonach i mieszaniny takich rozkładów.

In this paper we propose several classification rules based on data depth concept. We study a performance of the propositions on various multivariate data sets simulated from skewed, fat tailed distributions and mixtures of them.

{Depthproc} Package in Multivariate Time Series Mining

100%

Kosiorowski D. , Bocian M. , Węgrzynkiewicz A. , Zawadzki Z. , Cracow University of Economics

In this paper we present our novel R package {depthproc} which implements several multivariate statistical procedures induced by statistical depth functions and we discuss some examples and applications of the package in data mining concerning the multivariate time series.

W artykule przedstawiamy pakiet środowiska R naszego autorstwa o nazwie {DepthProc}. Pakiet zawiera implementacje kilku wielowymiarowych procedur statystycznych indukowanych przez statystyczne funkcje głębi. Przedstawiamy przykłady zastosowań pakietu w eksploracyjnej analizie wielowymiarowego szeregu czasowego.

Centrality-oriented causality. A study of EU agricultural subsidies and digital developement in Poland

88%

KOSIOROWSKI D. , RYDLEWSKI J. P.

tom 30

nr 3

47-63

Results of a convincing causal statistical inference related to socio-economic phenomena are treated as an especially desired background for conducting various socio-economic programs or gov-ernment interventions. Unfortunately, quite often real socio-economic issues do not fulfil restrictive assumptions of procedures of causal analysis proposed in the literature. This paper indicates certain empirical challenges and conceptual opportunities related to applications of procedures of data depth concept into a process of causal inference as to socio-economic phenomena. We show how to apply statistical functional depths to indicate factual and counterfactual distributions commonly used within procedures of causal inference. Thus, a modification of Rubin causality concept is proposed, i.e., a cen-trality-oriented causality concept. The presented framework is especially useful in the context of con-ducting causal inference based on official statistics, i.e., on the already existing databases. Methodo-logical considerations related to extremal depth, modified band depth, Fraiman-Muniz depth, and multivariate Wilcoxon sum rank statistic are illustrated by means of example related to a study of an impact of EU direct agricultural subsidies on digital development in Poland in the period 2012–2018.