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Novel approach to choosing principal components number in logistic regression

Vrigazova, Borislava - In: Proceedings of the ENTRENOVA - ENTerprise REsearch … 7 (2021) 1, pp. 1-12

the bootstrap procedure for model selection. We call this procedure the Bootstrapped-ANOVA PCA selection. Our results … combining principal components with the ANOVA selection. To improve the accuracy resulting from our automatic approach, we use …

Persistent link: https://www.econbiz.de/10013170122

ANOVA bootstrapped principal components analysis for logistic regression

Toleva, Borislava - In: Croatian review of economic, business and social … 8 (2022) 1, pp. 18-31

necessarily be ordered. The ANOVA bootstrapped PCA classification we propose is novel as it automatically selects the number of …Principal components analysis (PCA) is often used as a dimensionality reduction technique. A small number of principal … components is selected to be used in a classification or a regression model to boost accuracy. A central issue in the PCA is how …

Persistent link: https://www.econbiz.de/10013279710

Do maternal health problems influence child's worrying status? : evidence from British cohort study

Dai, Xianhua; Härdle, Wolfgang; Yu, Keming - 2014

The influence of maternal health problems on child's worrying status is important in practice in terms of the intervention of maternal health problems early for the influence on child's worrying status. Conventional methods apply symmetric prior distributions such as a normal distribution or a...

Persistent link: https://www.econbiz.de/10010253468

Factorisable sparse tail event curves

Chao, Shih-Kang; Härdle, Wolfgang; Yuan, Ming - 2015

In this paper, we propose a multivariate quantile regression method which enables localized analysis on conditional quantiles and global comovement analysis on conditional ranges for high-dimensional data. The proposed method, hereafter referred to as FActorisable Sparse Tail Event Curves, or...

Persistent link: https://www.econbiz.de/10011296776

Factorisable Multi-Task Quantile Regression

Härdle, Wolfgang K. - 2017

For many applications, analyzing multiple response variables jointly is desirable because of their dependency, and valuable information about the distribution can be retrieved by estimating quantiles. In this paper, we propose a multi-task quantile regression method that exploits the potential...

Persistent link: https://www.econbiz.de/10012966563

Bayesian treatment effects models with variable selection for panel outcomes with an application to earnings effects of maternity leave

Jacobi, Liana; Wagner, Helga; Frühwirth-Schnatter, Sylvia - 2014

Child birth leads to a break in a woman's employment history and is considered one reason for the relatively poor labor market outcomes observed for women compared to men. However, the time spent at home after child birth varies significantly across mothers and is likely driven by observed and,...

Persistent link: https://www.econbiz.de/10011346040

Variable Selection in Linear Regressions with Many Highly Correlated Covariates

Sharifvaghefi, Mahrad - 2022

This paper is concerned with variable selection in linear high-dimensional frameworks when the covariates under consideration are highly correlated. Existing methods in the literature generally require that the degree of correlation among covariates be weak, yet, often in applied research,...

Persistent link: https://www.econbiz.de/10014080925

LASSO-Penalized Clusterwise Linear Regression Modeling With Local Least Angle Regression (L-LARS)

Di Mari, Roberto; Rocci, Roberto; Gattone, Stefano Antonio - 2021

In clusterwise regression analysis, the goal is to predict a response variable based on a set of explanatory variables, each with cluster-specific effects. Nowadays, the number of candidates is typically large: whereas some of these variables might be useful, some others might contribute very...

Persistent link: https://www.econbiz.de/10013230005

Model selection in factor-augmented regressions with estimated factors

Djogbenou, Antoine A. - 2017

bootstrap approximation of the squared error of prediction of Shao (1996) to factor-augmented regressions. We show that this …

Persistent link: https://www.econbiz.de/10011756075

Efficient XVA Management : Pricing, Hedging, and Allocation Using Trade-Level Regression and Global Conditioning

Kenyon, Chris - 2014

Banks must manage their trading books, not just value them. Pricing includes valuation adjustments collectively known as XVA (at least credit, funding, capital and tax), so management must also include XVA. In trading book management we focus on pricing, hedging, and allocation of prices or...

Persistent link: https://www.econbiz.de/10013040052

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