arXiv subjects

Edoh Katchekpele

Publications and source records attributed to Edoh Katchekpele.

5 recordsLinked to original sources

arXiv2026

Long-Memory Estimation and Fractionally Integrated Modeling of White Maize Prices in Togo

Agricultural commodity prices often exhibit strong temporal persistence, which may limit the performance of conventional time series models. This study investigates long memory in logarithmic monthly white maize prices from six major markets in Togo between January 2001 and June 2022. Long memory is examined using the Geweke--Porter--Hudak, Local Whittle, Exact Local Whittle, and wavelet log-regression estimators. SARIMA, ARFIMA, and SARFIMA models are subsequently compared using the Bayesian Information Criterion and residual diagnostics. Long-range dependence is found across all markets. Fractionally integrated models provide the best fit for most markets, although SARIMA remains preferable for some. The results demonstrate that evidence of long memory does not necessarily imply that a fractionally integrated model provides the best empirical fit, emphasizing the importance of data-driven model selection.

stat.AP↗

arXiv2026

Asymptotic distribution of a robust wavelet-based NKK periodogram

This paper investigates the asymptotic distribution of a wavelet-based NKK periodogram constructed from least absolute deviations (LAD) harmonic regression at a fixed resolution level. Using a wavelet representation of the underlying time series, we analyze the probabilistic structure of the resulting periodogram under long-range dependence. It is shown that, under suitable regularity conditions, the NKK periodogram converges in distribution to a nonstandard limit characterized as a quadratic form in a Gaussian random vector, whose covariance structure depends on the memory properties of the process and on the chosen wavelet filters. This result establishes a rigorous theoretical foundation for the use of robust wavelet-based periodograms in the spectral analysis of long-memory time series with heavy-tailed inovations.

stat.ME↗

arXiv2025

Comparative analysis and practical applications of cubic transmutations for the Pareto distribution

Transmutation is a technique for extending classical probability distributions in order to give them more flexibility. In this paper, we are interested in cubic transmutations of the Pareto distribution. We establish a general formula that unifies existing cubic transmutations of the Pareto distribution and facilitates the derivation of new cubic transmutations that have not yet been explored in the literature. We also derive general formulas for the related mathematical properties. Finally, we perform a comparative analysis of the six transmutations existing in the literature using real-world data. The results obtained confirm the flexibility and effectiveness of cubic transmutations in modeling various types of data.

stat.ME↗

arXiv2025

Wavelet-based estimation of long-memory parameter in stochastic volatility models using a robust log-periodogram

In this paper, we propose a novel method for estimating the long-memory parameter in time series. By combining the multi-resolution framework of wavelets with the robustness of the Least Absolute Deviations (LAD) criterion, we introduce a periodogram providing a robust alternative to classical methods in the presence of non-Gaussian noise. Incorporating this periodogram into a log-periodogram regression, we develop a new estimator. Simulation studies demonstrate that our estimator outperforms the Geweke and Porter-Hudak (GPH) and Wavelet-Based Log-Periodogram (WBLP) estimators, particularly in terms of mean squared error, across various sample sizes and parameter configurations.

stat.ME↗

arXiv2025

Theoretical analysis and improvements in cubic transmutations of probability distributions

In statistics, processed data are becoming increasingly complex, and classical probability distributions are limited in their ability to model them. This is why, to better model data, extensive work has been conducted on extending classical probability distributions. Generally, this extension is achieved by transforming the cumulative distribution function of a baseline distribution through the addition of one or more parameters to enhance its flexibility. Cubic transmutation (CT) is one of the most popular methods for such extensions. However, CT does not have a unique definition because different approaches for CT have been proposed in the literature but are yet to be compared. The main goal of this paper is to compare these different approaches from both theoretical and empirical viewpoints. We study the relationships between the different approaches and we propose modified versions based on the extension of parameter ranges. The results are illustrated using Pareto distribution as baseline distribution.

stat.ME↗