penalized regressi
The case-crossover design via penalized regression
... The EuroSCAR study is attractive for this purpose for three reasons: firstly, it may potentially benefit from a penalization approach since it suffers from some of the challenges mentioned earlier. Secondly, even though ...
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Finite sample and asymptotic analysis of generalization ability with an application to penalized regression
... With a new sample at hand, GA is easily measured using validation or cross-validation to measure the goodness of fit of an estimated model on out-of-sample data. Without a new sample, however, it can be difficult to ...
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Efficient Implementation of Penalized Regression for Genetic Risk Prediction
... of penalized logistic regression (PLR) on modern datasets including hundreds of thousands of ...of penalized linear regression for quantitative ...Applying penalized linear regression to 350,000 ...
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A New Algorithm and Theory for Penalized Regression-based Clustering
... Though operating characteristics of PRclust have been intensively studied, its clustering consistency properties remain unknown. In this section, based on the maximum likelihood estimation framework, we develop some ...
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Quadratic programming and penalized regression
... If the choice of penalty is not clear beforehand, perhaps due to increased noise in the data, then the combined penalty approach of Section 3.1 may be used: if the signal function is smo[r] ...
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A Constructive Approach to $L_0$ Penalized Regression
... Another line of research concerns the greedy methods such as the orthogonal matching pursuit (OMP) (Mallat and Zhang, 1993) for solving (2) approximately. The main idea is to iteratively select one variable with the ...
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Penalized Regression and Model Selection in High Dimensions
... a penalized method, which refers to: (1)the true model is identified with probability tending to 1; (2) the estimation for nonzero coefficients is as efficient as if we know them in ...
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Penalized Splines Fitting for a Poisson Response Including Outliers
... The penalized regression splines used for the estimation of GAMs are presented in the ...three penalized splines, cubic regression splines, p-splines, thin- plate ...
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Penalized spline models and applications
... behind penalized regression methods is to quantify the notion of roughness of a curve through a suitable penalty functional and then to pose the estimation problem in a way that makes explicit the necessary ...
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Penalized Likelihood Analysis of Haplotype Effect.
... When performing ANOVA, the aim is first to determine which factors among a set of potential predictors significantly affect the response, and then characterize the inter- relationship among the levels within those ...
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Semiparametric stochastic volatility modelling using penalized splines
... maximum penalized likelihood approach exploits the strengths both of likelihood-based hidden Markov model (HMM) machinery and of penalized B-splines ...
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Concave Penalized Estimation of Sparse Gaussian Bayesian Networks
... the penalized log-likelihood, it would be interesting to compare more sophisticated optimization tech- niques such as adaptive and stochastic coordinate ...
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Double Penalized Quantile Regression in Partially Linear Models
... The rest of this paper is organized as follows. In Section 2, we first introduce the double-penalized quantile regression estimators in a partially linear regression model, and then propose an iterative algorithm ...
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Red Cards: Not Such Bad News For Penalized Guest Teams
... A soccer team is (like any kind of team) composed of different team members having to cooperate to achieve their common target - in this case to win the match. The match itself can be equated with the team task. Within a ...
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Penalized likelihood estimation of a trivariate additive probit model
... a penalized likelihood method to estimate a trivariate probit model, which accounts for several types of covariate effects (such as linear, nonlinear, random and spatial effects), as well as error ...a ...
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Penalized likelihood estimation of a trivariate additive probit model
... a penalized likelihood method to estimate a trivariate probit model, which accounts for several types of covariate effects (such as linear, nonlinear, random and spatial effects), as well as error ...a ...
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Penalized likelihood estimation of a trivariate additive probit model
... Broadly speaking, the GHK approach first applies a Cholesky decomposition on the model’s correlation matrix and then expresses the trivariate integrals as a product of three univariate p[r] ...
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Penalized Expectation Propagation for Graphical Models over Strings
... We present penalized expectation propaga- tion (PEP), a novel algorithm for approximate inference in graphical models. Expectation propagation is a variant of loopy belief prop- agation that keeps messages ...
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Confidence sets based on penalized maximum likelihood estimators
... of penalized maximum likelihood (least squares) estimators have been studied in the literature, mostly in the con- text of a …nite-dimensional linear regression model; see Knight and Fu (2000), Fan and Li (2001), ...
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Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy.
... The FDA models were also able to identify an association between concurrent cisplatin and carboplatin and severe acute dysphagia. The associations between concurrent chemotherapycisplatin and dysphagia in the MLR PLR ...
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