TOPICS

Data Analytics

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Statistics

Enhanced Lepage-type test statistics for location-scale shifts with right-skewed data

Detecting simultaneous shifts in location and scale between two populations is a common challenge in statistical inference, particularly in fields like biomedicine where right-skewed data distributions are prevalent. The classical Lepage test, which combines the Wilcoxon-Mann-Whitney and Ansari-Bradley tests, can be suboptimal under these conditions due to its restrictive assumptions of equal variances and medians. This study systematically evaluates enhanced Lepage-type test statistics that incorporate modern robust components for improved performance with right-skewed data. We combine the Fligner-Policello test and Fong-Huang variance estimator for the location component with a novel empirical variance estimator for the Ansari-Bradley scale component, relaxing assumptions of equal variances and medians. Extensive Monte Carlo simulations across exponential, gamma, chi-square, lognormal, and Weibull distributions demonstrate that tests incorporating both robust components achieve power

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Statistics

Scalable approximation of the transformation-free linear simplicial-simplicial regression via constrained iterative reweighted least squares

Simplicia-simplicial regression concerns statistical modeling scenarios in which both the predictors and the responses are vectors constrained to lie on the simplex. Fiksel et al. (2022) introduced a transformationfree linear regression framework for this setting, wherein the regression coefficients are estimated by minimizing the Kullback-Leibler divergence between the observed and fitted compositions, using an expectation-maximization (EM) algorithm for optimization. In this work, we reformulate the problem as a constrained logistic regression model, in line with the methodological perspective of Tsagris (2025), and we obtain parameter estimates via constrained iteratively reweighted least squares. Simulation results indicate that the proposed procedure substantially improves computational efficiency-yielding speed gains ranging from 6×−−326×-while providing estimates that closely approximate those obtained from the EM-based approach.

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Statistics

The α–regression for compositional data: a unified framework for standard, spatially-lagged, spatial autoregressive and geographically-weighted regression models

Compositional data–vectors of non-negative components summing to unity–frequently arise in scientific applications where covariates influence the relative proportions of components, yet traditional regression approaches face challenges regarding the unit-sum constraint and zero values. This paper revisits the α–regression framework, which uses a flexible power transformation parameterized by α to interpolate between raw data analysis and log-ratio methods, naturally handling zeros without imputation while allowing data-driven transformation selection. We formulate α–regression as a non-linear least squares problem, study its asymptotic properties, provide efficient estimation via the Levenberg-Marquardt algorithm, and derive marginal effects for interpretation.

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Statistics

Comparison of discriminant analysis methods on the sphere

We compared maximum likelihood and the k-NN algorithm in the context of discriminant analysis with spherical data.

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Statistics

Discriminant Analysis with Spherical Data

Discriminant analysis for spherical data, or directional data in general, has not been extensively studied, and most papers focus on one distribution, the von Mises-Fisher. In this work, we study more distributions, escaping the rotational symmetry bound of the aforementioned distribution and also include a non parametric classier, the k-NN algorithm.

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Statistics

A Folded Model for Compositional Data Analysis

A folded type model is developed for analyzing compositional data based that provides a new and flexible class of distributions for modeling data defined on the simplex sample space. Despite its rather seemingly complex structure, employment of the EM algorithm guarantees efficient parameter estimation.

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Bibliometrics

Measuring the Publishing Productivity of Economics Departments in Europe

We measure performance on the basis of a publishing productivity index which allows to account for difference in research inputs among departments.

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Econometrics

Testing the Linearity of a Time Series

This letter proposes a simple test for the linearity of a time series. We compare the small and large samples properties of the suggested test via Monte Carlo techniques with well known time domain linearity tests. Our results suggest that the suggested test over performs the power of the other competitive tests in small samples.

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Econometrics

Temporal Aggregation and Systematic Sampling Effects on Non Linear Granger Causality Tests between Trade Volume and Returns. Some Monte Carlo and Empirical Results from the Athens Stocks Exchange.

In this paper we investigate the effects of temporal aggregation and systematic sampling using some well known linear and nonlinear Granger causality tests.

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Econometrics

Temporal Aggregation and the Ramsey's Test for Functional Form: Results from Empirical and Monte Carlo experiment

This short paper demonstrates that the use of temporally aggregated data may affect the power and the size of the well known the Ramsey's (1969) RESET test.

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Econometrics

Non-Linear Diachronic Interaction Between the Advance/Decline Ratio Index and the Returns of the Market's General Index: Empirical Evidence from the Athens Stock Exchange

This paper examines the existence of a linear or nonlinear interaction between the Advance/Decline ratio index and the returns of the Athens General Index.

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Labour Economics

Unemployment Dynamics in the Greek Crisis

This short empirical paper examines the unemployment dynamics in Greece both in the long run and during the current crisis.

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Econometrics

Forecasting Mortality Rate Using a Neural Network with Fuzzy Inference System

Various methods have been developed to improve mortality forecasts. The authors proposed a neuro-fuzzy model to forecast the mortality. The forecasting of mortality is curried out by an ANFIS model which uses a first order Sugeno-type FIS.

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Econometrics

Forecasting Unemployment Rate Using a Neural Network with Fuzzy Inference System

The paper presents a new technique in the field of unemployment modeling in order to forecast unemployment index. Techniques from the Artificial Neural Networks and from fuzzy logic have been combined to generate a neuro-fuzzy model.

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Macroeconomics

Consumer Price Index. Does the Price Collection Frequency Matter? Some Monte Carlo Results.

In this paper, we examine the effects of data collection frequency on the computation of the Consumer Price Index (CPI).

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Econometrics

A Note on Missing Data Effects on the Hausman (1978) Simultaneity Test: Some Monte Carlo Results.

This short paper demonstrates the effects of using missing data on the power of the well-known Hausman (1978) test for simultaneity in structural econometric models.

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Macroeconomics

The Impact of the Advance of SME's for the Romanian Economy

The aim of the paper is to determine the state of IT within the Romanian organizations and its impact for the Romanian economy.

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Finance

Nonlinear Diachronic Effects Between Stock Returns and Mutual Fund Flows: Additional Empirical Evidence from the Athens Stocks Exchange

This short paper examines the nonlinear interaction between mutual fund flows and stock returns in Greece. We investigate the possibility of a nonlinear causality mechanism through which mutual funds flows may affect stock returns and vice versa.

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Econometrics

Temporal Aggregation Effects in Choosing the Optimal Lag Order in Stable ARMA Models: Some Monte Carlo Results

A crucial aspect of empirical research based on ARIMA(p,q) model is the choice of the appropriate lag order. Several criteria have been used in order to identify the appropriate order of a ARIMA(p,q) process. In this paper we investigate the effects of using a variation of selection criteria under different temporal aggregation levels.

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Econometrics

Testing the Linearity of a Time Series. Some Monte-Carlo and Empirical Tests

This letter proposes a simple test for the linearity of a time series. We compare the small and large samples properties of the suggested test via Monte Carlo techniques with well known time domain linearity tests.

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Macroeconomics

A Note on the Diachronic Behaviour of the OECD Forecasts for Greece

In this short paper a Gamma distributed lags model is used to study the diachronic responses between the actual data and the forecasts supplied by OECD the last 27 years for the case of the Greek Economy.

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Econometrics

Conventional Nonlinear Relationships between GDP, Inflation and Stock Market Returns. An Investigation for the Greek Economy

This paper is using simple nonlinearity tests to provide evidence of a positive and significant causal relationship going from stock market development to economic growth in Greece during the last 10 years.

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