Results 31 to 40 of about 674 (121)
Sparse least trimmed squares regression. [PDF]
Sparse model estimation is a topic of high importance in modern data analysis due to the increasing availability of data sets with a large number of variables. Another common problem in applied statistics is the presence of outliers in the data.
Croux, Christophe +2 more
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(Non) Linear Regression Modeling [PDF]
We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1, . . .
Čížek, Pavel
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, 2023 The analysis of the high-dimensional dataset when the number of explanatory variables is greater than the observations using classical regression approaches is not applicable and the results may be misleading.
Mahdi Roozbeh +5 more
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Robust and sparse factor modelling. [PDF]
Factor construction methods are widely used to summarize a large panel of variables by means of a relatively small number of representative factors. We propose a novel factor construction procedure that enjoys the properties of robustness to outliers and
Croux, Christophe, Exterkate, Peter
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Adjusting the penalized term for the regularized regression models
, 2018 More attention has been given to regularization methods in the last two decades as a result of exiting high-dimensional ill-posed data. This paper proposes a new method of introducing the penalized term in regularized regression.
Haggag, Magda M.M.
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Modeling for optimal probability prediction
, 2002 We present a general modelling method for optimal probability prediction over future observations, in which model dimensionality is determined as a natural by-product.
Witten, Ian H., Wang, Yong
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Robust sparse principal component analysis. [PDF]
A method for principal component analysis is proposed that is sparse and robust at the same time. The sparsity delivers principal components that have loadings on a small number of variables, making them easier to interpret.
Croux, Christophe +2 more
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A comparison of shrinkage methods in linear regression
, 2020 İstatistiksel araştırmalar ve analizlerde veri kümesine dayalı olarak elde edilen regresyon modelleri çok önemli rol oynar. Bu modellerden biride çoklu doğrusal regresyon modelidir.
Kalkan, Erdem
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Improving estimations in quantile regression model with autoregressive errors
, 2018 An important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and ...
Bahadır Yuzbasi +7 more
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Polychotomous logistic regression via the Lasso
, 1999 grantor: University of TorontoThe maximum likelihood method is traditionally used in estimating parameters in polychotomous logistic regression.
Mak, Carmen
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