Search results for " selection"

showing 10 items of 1271 documents

The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression.

2020

This paper focuses on hypothesis testing in lasso regression, when one is interested in judging statistical significance for the regression coefficients in the regression equation involving a lot of covariates. To get reliable p-values, we propose a new lasso-type estimator relying on the idea of induced smoothing which allows to obtain appropriate covariance matrix and Wald statistic relatively easily. Some simulation experiments reveal that our approach exhibits good performance when contrasted with the recent inferential tools in the lasso framework. Two real data analyses are presented to illustrate the proposed framework in practice.

Statistics and ProbabilityStatistics::TheoryInduced smoothingEpidemiologyComputer scienceFeature selectionWald test01 natural sciencesasthma researchStatistics::Machine Learning010104 statistics & probability03 medical and health sciencesHealth Information ManagementLasso (statistics)Linear regressionsparse modelsStatistics::MethodologyComputer Simulation0101 mathematicssandwich formula030304 developmental biologyStatistical hypothesis testing0303 health sciencesCovariance matrixlung functionRegression analysisStatistics::Computationsparse modelResearch DesignAlgorithmSmoothingvariable selectionStatistical methods in medical research
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Selecting the tuning parameter in penalized Gaussian graphical models

2019

Penalized inference of Gaussian graphical models is a way to assess the conditional independence structure in multivariate problems. In this setting, the conditional independence structure, corresponding to a graph, is related to the choice of the tuning parameter, which determines the model complexity or degrees of freedom. There has been little research on the degrees of freedom for penalized Gaussian graphical models. In this paper, we propose an estimator of the degrees of freedom in $$\ell _1$$ -penalized Gaussian graphical models. Specifically, we derive an estimator inspired by the generalized information criterion and propose to use this estimator as the bias term for two informatio…

Statistics and ProbabilityStatistics::TheoryKullback–Leibler divergenceKullback-Leibler divergenceComputer scienceGaussianInformation Criteria010103 numerical & computational mathematicsModel complexityModel selection01 natural sciencesTheoretical Computer Science010104 statistics & probabilitysymbols.namesakeStatistics::Machine LearningGeneralized information criterionEntropy (information theory)Statistics::MethodologyGraphical model0101 mathematicsPenalized Likelihood Kullback-Leibler Divergence Model Complexity Model Selection Generalized Information Criterion.Model selectionEstimatorStatistics::ComputationComputational Theory and MathematicsConditional independencesymbolsPenalized likelihoodStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaAlgorithmStatistics and Computing
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Clusters of effects curves in quantile regression models

2018

In this paper, we propose a new method for finding similarity of effects based on quantile regression models. Clustering of effects curves (CEC) techniques are applied to quantile regression coefficients, which are one-to-one functions of the order of the quantile. We adopt the quantile regression coefficients modeling (QRCM) framework to describe the functional form of the coefficient functions by means of parametric models. The proposed method can be utilized to cluster the effect of covariates with a univariate response variable, or to cluster a multivariate outcome. We report simulation results, comparing our approach with the existing techniques. The idea of combining CEC with QRCM per…

Statistics and ProbabilityStatistics::TheoryMultivariate statistics05 social sciencesUnivariateFunctional data analysis01 natural sciencesQuantile regressionQuantile regression coefficients modeling Multivariate analysis Functional data analysis Curves clustering Variable selection010104 statistics & probabilityComputational Mathematics0502 economics and businessParametric modelCovariateStatistics::MethodologyApplied mathematics0101 mathematicsStatistics Probability and UncertaintyCluster analysisSettore SECS-S/01 - Statistica050205 econometrics MathematicsQuantile
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Structure Learning in Nested Effects Models

2007

Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g., the effects showing in gene expression profiles or as morphological features of the perturbed cell. In this paper we expand the statistical basis of NEMs in four directions. First, we derive a new formula for the likelihood function of a NEM, which generalizes previous results for binary data. Second, we prove model identifiability under mild assumptions. Third, we show that the new formulation of the likelihood allows efficiency in traversing model space. Fourth, we…

Statistics and ProbabilityTraverseComputer scienceMolecular Networks (q-bio.MN)Genes MHC Class IIPerturbation (astronomy)Genes InsectFeature selectionQuantitative Biology - Quantitative Methods03 medical and health sciences0302 clinical medicineGeneticsAnimalsheterocyclic compoundsQuantitative Biology - Molecular NetworksGraphical modelMolecular BiologyQuantitative Methods (q-bio.QM)Oligonucleotide Array Sequence Analysis030304 developmental biologyLikelihood Functions0303 health sciencesNanoelectromechanical systemsModels StatisticalModels GeneticGene Expression ProfilingGenomicsComputational MathematicsDrosophila melanogasterPhenotypeFOS: Biological sciencesBinary dataIdentifiabilityRNA InterferenceLikelihood functionAlgorithmAlgorithms030217 neurology & neurosurgery
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Efficient change point detection in genomic sequences of continuous measurements

2010

Abstract Motivation: Knowing the exact locations of multiple change points in genomic sequences serves several biological needs, for instance when data represent aCGH profiles and it is of interest to identify possibly damaged genes involved in cancer and other diseases. Only a few of the currently available methods deal explicitly with estimation of the number and location of change points, and moreover these methods may be somewhat vulnerable to deviations of model assumptions usually employed. Results: We present a computationally efficient method to obtain estimates of the number and location of the change points. The method is based on a simple transformation of data and it provides re…

Statistics and Probabilitymodel selectionBreast Neoplasmscomputer.software_genreBiochemistryCell LineSimple (abstract algebra)Cell Line TumorHumansComputer Simulationpiecewise constant modelMolecular BiologyMathematicsOligonucleotide Array Sequence AnalysisSupplementary dataComparative Genomic HybridizationModels StatisticalSeries (mathematics)Model selectionGenomicsComputer Science ApplicationsComputational MathematicsR packageTransformation (function)Computational Theory and MathematicsChange pointsChangepointaCGH analysiFemaleData miningSettore SECS-S/01 - StatisticacomputerChange detection
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Contributed discussion on article by Pratola

2016

The author should be commended for his outstanding contribution to the literature on Bayesian regression tree models. The author introduces three innovative sampling approaches which allow for efficient traversal of the model space. In this response, we add a fourth alternative.

Statistics and Probabilitymodel selectionMarkov Chain Monte Carlo (MCMC)Bayesian regression treeComputer scienceBig dataBayesian regression tree (BRT) modelsComputingMilieux_LEGALASPECTSOFCOMPUTINGbirth–death processMachine learningcomputer.software_genreSequential Monte Carlo methods01 natural sciencespopulation Markov chain Monte Carlo010104 statistics & probabilitysymbols.namesakebig data0502 economics and businessBayesian Regression Trees (BART)0101 mathematics050205 econometrics Bayesian treed regressionMultiple Try Metropolis algorithmsINFERÊNCIA ESTATÍSTICAbusiness.industryApplied MathematicsModel selection05 social sciencesRejection samplingData scienceVariable-order Bayesian networkTree (data structure)Tree traversalMarkov chain Monte Carlocontinuous time Markov processsymbolsArtificial intelligencebusinessBayesian linear regressioncommunication-freecomputerGibbs samplingBayesian Analysis
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Customer value in Quick-Service Restaurants: A cross-cultural study

2020

Abstract In spite of the relativistic nature of Customer Value concept, research on differences in Value perceptions across cultures is still scarce. Gaining insight about this issue would be especially relevant for highly competitive and globalized industries such as Fast-Food or Quick-Service Restaurants (QSR). The purpose of the present paper is to identify Value dimensions in this industry and to analyze the links between dimensions of Value, Satisfaction and Loyalty, testing the consistency of the results obtained across three different countries. To achieve this aim, after one in-depth interview with a QSR manager and two intercultural focus groups with QSR customers, a questionnaire …

Strategy and Managementmedia_common.quotation_subject05 social sciencesCultural group selectionFocus groupTourism Leisure and Hospitality ManagementPerceptionCustomer value0502 economics and businessLoyaltyCross-cultural050211 marketingBusinessMarketing050203 business & managementConsumer behaviourmedia_commonValuation (finance)International Journal of Hospitality Management
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Does the addition of single joint exercises to a resistance training program improve changes in performance and anthropometric measures in untrained …

2018

The present study compared changes in muscle performance and anthropometric measures in young men performing resistance training (RT) programs composed of only multi joint (MJ) exercises, or with the addition of single joint (SJ) exercises (MJ+SJ). Twenty untrained men were randomized to MJ or MJ+SJ groups for 8 weeks. Both groups performed the same MJ exercises. The difference was that the MJ+SJ group added SJ exercises for upper and lower limbs. Participants were tested for 10 repetitions maximum (10RM), flexed arm circumference, and biceps and triceps skinfolds. Both groups significantly increased 10RM load for the bench press (MJ 38.5%, MJ+SJ 40.1%), elbow extension (MJ 28.7%, MJ+SJ 31.…

Strength traininglcsh:MedicineBench pressBicepslcsh:QM1-69503 medical and health sciences0302 clinical medicineMedicineOrthopedics and Sports MedicineTraining volumeIsolation exerciseLeg pressMolecular BiologyMulti jointbusiness.industrylcsh:RSignificant differenceResistance traininglcsh:Human anatomy030229 sport sciencesCell BiologyAnthropometryExercise selectionExercise selection; Isolation exercise; Muscle hypertrophy; Strength training; Training volume; Neurology (clinical); Orthopedics and Sports Medicine; Cell Biology; Molecular BiologyMuscle hypertrophyNeurology (clinical)Strength trainingbusinessNuclear medicine030217 neurology & neurosurgeryEuropean Journal of Translational Myology
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Data from: Gray plumage color is more cryptic than brown in snowy landscapes in a resident color polymorphic bird

2020

Camouflage may promote fitness of given phenotypes in different environments. The tawny owl (Strix aluco) is a colour polymorphic species with a grey and brown morph resident in the Western Palearctic. A strong selection pressure against the brown morph during snowy and cold winters has been documented earlier but the selection mechanisms remain unresolved. Here we hypothesise that selection favors the grey morph because it is better camouflaged against predators and mobbers in snowy conditions compared to the brown one. We conducted an online citizen science experiment where volunteers were asked to locate a grey or a brown tawny owl specimen from pictures taken in snowy and snowless lands…

Strix alucocamouflagecolor polymorphismvisual predationPolymorphic speciessurvival selection
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Not all bull and bear markets are alike: insights from a five-state hidden semi-Markov model

2022

This paper employs the hidden semi-Markov model and a novel model selection procedure to detect different states in the US stock market. The empirical results suggest that the market is switching between five states that can be classified into three bull states and two bear states. The three bull states are categorized as a low volatility bull market, a high volatility bull market, and a stock market bubble. One of the bear states represents a regular bear market, while the other one corresponds to either a stock market crash or a market correction. The paper demonstrates that the five-state model is consistent with a number of stylized facts and provides many valuable insights into the dyn…

Stylized factEconomics and EconometricsModel selectionStrategy and ManagementStock market bubbleStock market crashEconometricsEconomicsStock marketHidden semi-Markov modelMarket correctionVolatility (finance)Business and International ManagementFinanceRisk Management
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