نتایج جستجو برای: partial linear model preliminary test lasso
تعداد نتایج: 3367252 فیلتر نتایج به سال:
The solution path of the 1D fused lasso for an ndimensional input is piecewise linear with O(n) segments [1], [2]. However, existing proofs of this bound do not hold for the weighted fused lasso. At the same time, results for the generalized lasso, of which the weighted fused lasso is a special case, allow Ω(3) segments [3]. In this paper, we prove that the number of segments in the solution pa...
A Bayesian approach to variable selection which is based on the expected Kullback–Leibler divergence between the full model and its projection onto a submodel has recently been suggested in the literature. For generalized linear models an extension of this idea is proposed by considering projections onto subspaces defined via some form of L1 constraint on the parameter in the full model. This l...
abstract: in this thesis, we focus to class of convex optimization problem whose objective function is given as a linear function and a convex function of a linear transformation of the decision variables and whose feasible region is a polytope. we show that there exists an optimal solution to this class of problems on a face of the constraint polytope of feasible region. based on this, we dev...
Classical statistical theory offers validity under restricted assumptions. However, in practice, it is a common approach to perform statistical analysis based on data-driven model selection [1], which guarantees none of results of classical statistical theory. Those results include hypothesis testings and confidence intervals which are useful tools of measuring fitness of models. Considering th...
Numerous researches have been carried out to explain the relationship between the count data y and numbers of covariates x through a generalized linear model (GLM). This paper proposes a hierarchical Bayesian LASSO solution using six different prior models to the negative binomial regression. Latent variables Z have been introduced to simplify the GLM to a standard linear regression model. The ...
Abstract. Statistical learning methods offer a promising approach for low-flow regionalization. We examine seven statistical models (Lasso, linear, and nonlinear-model-based boosting, sparse partial least squares, principal component regression, random forest, support vector regression) the prediction of winter summer low flow based on hydrologically diverse dataset 260 catchments in Austria. I...
This study develops a data-driven group variable selection method for data envelopment analysis (DEA), a non-parametric linear programming approach to the estimation of production frontiers. The proposed method extends the group Lasso (least absolute shrinkage and selection operator) designed for variable selection on (often predefined) groups of variables in linear regression models to DEA mod...
PURPOSE The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. METHODS AND MATERIALS Quality of life questionnaire datasets from 206 patients with HNC were a...
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