scholarly journals Introducing LASSO-type penalisation to generalised joint regression modelling for count data

2021 ◽  
Author(s):  
Hendrik van der Wurp ◽  
Andreas Groll
Keyword(s):  
Estimation Algorithm ◽  
R Package ◽  
Adaptive Lasso ◽  
High Dimensional ◽  
World Cup ◽  
Fifa World Cup ◽  
Regression Modelling ◽  
Count Response ◽  
Regression Framework ◽  
Empirical Performance

AbstractIn this work, we propose an extension of the versatile joint regression framework for bivariate count responses of the package by Marra and Radice (R package version 0.2-3, 2020) by incorporating an (adaptive) LASSO-type penalty. The underlying estimation algorithm is based on a quadratic approximation of the penalty. The method enables variable selection and the corresponding estimates guarantee shrinkage and sparsity. Hence, this approach is particularly useful in high-dimensional count response settings. The proposal’s empirical performance is investigated in a simulation study and an application on FIFA World Cup football data.

scholarly journals Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data

2019 ◽  
Vol 36 (6) ◽  
pp. 1785-1794
Author(s):  
Jun Li ◽  
Qing Lu ◽  
Yalu Wen
Keyword(s):  
Risk Prediction ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
R Package ◽  
Kernel Functions ◽  
Adaptive Lasso ◽  
Supplementary Information ◽  
High Dimensional ◽  
Omics Data ◽  
Modeling Framework

Abstract Motivation The use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed. Results We present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regions as well as predictive layers of omics data, and achieves robust selection performance. Through extensive simulation studies, the analyses of PET-imaging outcomes from the Alzheimer’s Disease Neuroimaging Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently outperforms competing methods in phenotype prediction. Availability and implementation The R-package is available at https://github.com/YaluWen/OmicPred. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Sparse classification with paired covariates

2019 ◽  
Vol 14 (3) ◽  
pp. 571-588
Author(s):  
Armin Rauschenberger ◽  
Iuliana Ciocănea-Teodorescu ◽  
Marianne A. Jonker ◽  
Renée X. Menezes ◽  
Mark A. van de Wiel
Keyword(s):  
Predictive Models ◽  
R Package ◽  
The Other ◽  
Adaptive Lasso ◽  
High Dimensional ◽  
Classification Problems ◽  
One To One ◽  
Single Response ◽  
Sparse Classification

AbstractThis paper introduces the paired lasso: a generalisation of the lasso for paired covariate settings. Our aim is to predict a single response from two high-dimensional covariate sets. We assume a one-to-one correspondence between the covariate sets, with each covariate in one set forming a pair with a covariate in the other set. Paired covariates arise, for example, when two transformations of the same data are available. It is often unknown which of the two covariate sets leads to better predictions, or whether the two covariate sets complement each other. The paired lasso addresses this problem by weighting the covariates to improve the selection from the covariate sets and the covariate pairs. It thereby combines information from both covariate sets and accounts for the paired structure. We tested the paired lasso on more than 2000 classification problems with experimental genomics data, and found that for estimating sparse but predictive models, the paired lasso outperforms the standard and the adaptive lasso. The R package is available from cran.

Selecting the best team from the players in the 2018 FIFA World Cup using performance analysis

2020 ◽  
Vol 42 (1) ◽  
pp. 15-32
Author(s):  
Tugay Karadag ◽  
Coskun Parim ◽  
Erhan Cene
Keyword(s):  
Performance Analysis ◽  
Topsis Method ◽  
World Cup ◽  
Fifa World Cup ◽  
Performance Variables

This study aims to determine the best player in each position from among the footballers who played in the 2018 World Cup in Russia. Player statistics for those who played over 200 minutes were obtained from the FIFA official and transfermarkt.com websites. Selected performance variables were then calculated per 100 minutes and the results were normalised. Kruskal Wallis H and Bonferroni Tests were used to determine the weights of the variables before the analysis. As the variables will have different values according to the players’ positions, the weights for each position were calculated separately. Finally, the performances of the players on the basis of the variables used were ranked for each position using the TOPSIS method. A second analysis was undertaken including only those players whose ages were under 28 and goalkeepers whose ages were under 32. The purpose of this analysis was to identify players with potential that had been largely unrecognised up until the tournament. It was found that both the teams selected in this way were dominated by players from European clubs. Ninety-two percent of the top sixty players in the analysis were playing in European leagues with 85% playing in Spain, England, Italy, Germany, France or Russia.

scholarly journals Price discrimination in informal labor markets in Bogotá: an audit experiment during the 2018 FIFA World Cup

2021 ◽  
Vol 55 (1) ◽  
Author(s):  
Paula Zamora ◽  
César Mantilla ◽  
Mariana Blanco
Keyword(s):  
Labor Markets ◽  
Price Discrimination ◽  
Pricing Strategies ◽  
World Cup ◽  
Street Vendors ◽  
Fifa World Cup ◽  
Informal Labor ◽  
Seasonal Demand ◽  
Within Subjects ◽  
Gender Based

AbstractWe conducted an audit experiment to examine whether street vendors in Bogotá (Colombia) exert price discrimination based on buyers’ attributes, such as gender and nationality, and based on product characteristics, such as the increasing marginal valuation of items needed to complete a collection. We exploited the seasonal demand for album stickers related to the FIFA World Cup Russia 2018. In our within-subjects design, experimenters carried out in-person audits and quoted a pre-determined list of missing stickers. They interacted with 59 sticker vendors located in five geographic clusters and collected 287 vendor–buyer interactions. We find that prices quoted to foreign buyers are higher than prices quoted to Colombian buyers. By contrast, we do neither find evidence supporting direct gender-based discrimination, nor that vendors charge a higher price per sticker when the list of missing stickers is shorter. We complement the study with a qualitative analysis based on interviews that reveal vendors’ pricing strategies, their awareness of price discrimination, and the trade of counterfeits. The qualitative results suggest that price discrimination appears to be unconscious.

scholarly journals Kicking off: Is the association between the FIFA world cup and domestic abuse an international phenomenon?

2021 ◽  
pp. 0032258X2110071
Author(s):  
Stuart Kirby ◽  
Nathan Birdsall
Keyword(s):  
Violence Against Women ◽  
Partner Violence ◽  
Medical Records ◽  
Domestic Abuse ◽  
Intimate Partner ◽  
World Cup ◽  
Fifa World Cup ◽  
The Uk ◽  
Medical Examinations ◽  
International Phenomenon

This study examines whether increases in incidents of female domestic abuse occur during FIFA world cup tournaments, in countries, other than the UK. Columbian medical records providing national daily counts, relating to Violence Against Women (VAW) and females subject to Intimate Partner Violence (IPV), across two world cup tournaments (2014/2018) were analysed. The number of medical examinations rose by 43% (VAW) and 39% (IPV) during the 2014 Columbia match days, and 26% (VAW) and 27% (IPV) during the 2018 match days, when compared to non-match days (p < .001). The increases were higher on a weekend and when winning, rather than losing.

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