Parametric and Nonparametric Statistics: From Theory to Applications

Dear Colleagues,

Parametric methods in statistics are based on hypothetical parametric models of the data under study. This requires that the assumptions made about these models be verified for the reliability of the results obtained with these methods to be confirmed. In contrast, nonparametric methods in statistics are not based on hypothetical models. They can be used even if the assumptions required for the parametric methods are not met. For choosing between parametric or nonparametric methods one should consider several criteria about the data and the assumptions, and ensure to check the validity of these assumptions. Semiparametric methods can be seen as those combining parametric and nonparametric principles and techniques.

Parametric and nonparametric methods in statistics are widely studied in literature. Their theoretical developments and practical applications are growing steadily. This Special Issue, entitled “Parametric and Nonparametric Statistics: From Theory to Applications”, is dedicated to topics involving recent developments in parametric, semiparametric and nonparametric statistics, and their application to various scientific domains such as time series, regression, empirical processes, high-dimensional data, data mining., econometrics, finance, biology, signal, image processing, etc. Thus, this Special Issue is a platform for researchers and readers interested to these topics.

We invite papers on parametric or nonparametric estimation, on parametric or nonparametric tests, as well as those on semiparametric methods. This includes review papers, theoretical and methodological papers, and also numerical papers treating simulated and/or real data with parametric, nonparametric or semiparametric statistical methods.

Prof. Dr. Joseph Ngatchou-Wandji
Guest Editor

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