AUTHOR

Tsagris Michail

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Econometrics , Applied Research - 2024

Predicting Full Retirement Attainment of NBA Players

The aim of this analysis is to predict whether an NBA player will be active in the league for at least 10 years so as to be qualified for NBA’s full retirement scheme which allows for the maximum benefit payable by law. We collected per game statistics for players during their second year, drafted during the years 1999 up to 2006, for which information on their career longetivity is known. By feeding these statistics of the sophomore players into statistical and machine learning algorithms we select the important statistics and manage to accomplish a satisfactory predictability performance. Further, we visualize the effect of each of the selected statistics on the estimated probability of staying in the league for more than 10 years

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Statistics , Theory - 2024

Constrained Least Squares Simplicial-Simplicial Regression

Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. For this setting, constrained least squares, where the regression coefficients themselves lie within the simplex, is proposed. The model is transformation-free but the adoption of a power transformation is straightforward, it can treat more than one compositional datasets as predictors and offers the possibility of weights among the simplicial predictors. Among the model’s advantages are its ability to treat zeros in a natural way and a highly computationally efficient algorithm to estimate its coefficients. Resampling based hypothesis testing procedures are employed regarding inference, such as linear independence, and equality of the regression coefficients to some pre-specified values. The performance of the proposed technique and its comparison to an existing methodology that is of the same spirit takes place u

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Agricultural Economics , Applied Research - 2024

Using Synthetic Farm Data to Estimate Individual Nitrate Leaching Levels

he paper develops a novel synthetic population generation scheme to deal with the NPS pollution problem of nitrate leaching from agricultural farms. The scheme relies upon estimation of the joint distribution of the variables using Bayesian network learning which, coupled with the use of non-parametric regression models facilitate the generation of realistic synthetic populations. Then building upon the sequential GME model suggested by Kaplan et al., (2003) in line with the multiple production relations model suggested by Murty et al., (2012) we obtain econometric estimates of both the production technology and nature's residual generating mechanism for the synthetic population of farms. These estimates are used to proxy a reliable optimal taxation scheme that corresponds to local environmental and economic conditions. The methodology is applied to the Greek FADN dataset for the Greek NUTS II region of Thessaly during the 2017-18 cropping year.

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Statistics , Applied Research - 2023

Cauchy Robust Principal Component Analysis with Applications to High-Dimensional Data Sets

In this paper, we propose a modified formulation of the principal components analysis, based on the use of a multivariate Cauchy likelihood instead of the Gaussian likelihood, which has the effect of robustifying the principal components. We present an algorithm to compute these robustified principal components. We additionally derive the relevant influence function of the first component and examine its theoretical properties.

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Statistics , Applied Research - 2023

Circular and Spherical Projected Cauchy Distributions

Two new distributions are proposed: the circular projected and the spherical projected Cauchy distributions. A special case of the circular projected Cauchy coincides with the wrapped Cauchy distribution, and for this, a generalization is suggested that offers better fit via the inclusion of an extra parameter. For the spherical case, by imposing two conditions on the scatter matrix we end up with an elliptically symmetric distribution.

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Statistics , Applied Research - 2023

Flexible Non-parametric Regression Models for Compositional Response Data with Zeros

Compositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. When parametric assumptions do not hold or are difficult to verify, non-parametric regression models can provide a convenient alternative method for prediction. To this end, we consider an extension to the classical k-NN regression, termed a-k-NN regression, that yields a highly flexible non-parametric regression model for compositional data through the use of the a-transformation.

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Agricultural Economics , Applied Research - 2023

Factors Shaping Innovative Behavior: A Meta-Analysis of Technology Adoption Studies in Agriculture

In this paper, we employ a meta-regression analysis approach to synthesize empirical evidence on the average partial effects of eleven adoption determinants that regularly appear in empirical studies examining farmer's adoption behavior worldwide. Our analysis considers a total of 122 studies from the adoption literature using discrete choice models that are published in 24 peer-reviewed journals since 1985, covering farmer's adoption behavior around the world and for a wide variety of agricultural technologies.

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Agricultural Economics , Applied Research - 2021

Nitrate Pollution and Efficiency Measurement in Intensive Farming Systems: A Parametric By-Production Technology Approach

This paper develops a novel empirical framework for estimating individual emission levels in a nonpoint source (NPS) pollution problem. For doing so we incorporate into the GME model suggested by Kaplan et al., (2003) a specific theoretical structure describing both crop production technology and nature's residual generating mechanism based on the multiple production relations model suggested by Murty et al, (2012) fitted into a parametric stochastic framework.

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Statistics , Data Analytics - 2019

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 , Theory - 2018

Modelling structural zeros in compositional data via a zero-censored multivariate normal model

We present a new model for analyzing compositional data with structural zeros. Inspired by \cite{butler2008} who suggested a model in the presence of zero values in the data we propose a model that treats the zero values in a different manner. Instead of projecting every zero value towards a vertex, we project them onto their corresponding edge and fit a zero-censored multivariate model.

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Statistics , Data Analytics - 2018

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 , Data Analytics - 2018

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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