Search results for " statistics."

showing 10 items of 1749 documents

Prioritizing covariates in the planning of future studies in the meta-analytic framework

2016

Science can be seen as a sequential process where each new study augments evidence to the existing knowledge. To have the best prospects to make an impact in this process, a new study should be designed optimally taking into account the previous studies and other prior information. We propose a formal approach for the covariate prioritization, i.e., the decision about the covariates to be measured in a new study. The decision criteria can be based on conditional power, change of the p-value, change in lower confidence limit, Kullback-Leibler divergence, Bayes factors, Bayesian false discovery rate or difference between prior and posterior expectation. The criteria can be also used for decis…

0301 basic medicineStatistics and ProbabilityFalse discovery rateComputer scienceBayesian probabilityBayes factorGeneral MedicineMultiple-criteria decision analysis01 natural sciencesConfidence interval010104 statistics & probability03 medical and health sciences030104 developmental biologySample size determinationCovariateEconometrics0101 mathematicsStatistics Probability and UncertaintyDivergence (statistics)Biometrical Journal
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A generalization of Kingman's model of selection and mutation and the Lenski experiment.

2017

Kingman’s model of selection and mutation studies the limit type value distribution in an asexual population of discrete generations and infinite size undergoing selection and mutation. This paper generalizes the model to analyze the long-term evolution of Escherichia. coli in Lenski experiment. Weak assumptions for fitness functions are proposed and the mutation mechanism is the same as in Kingman’s model. General macroscopic epistasis are designable through fitness functions. Convergence to the unique limit type distribution is obtained.

0301 basic medicineStatistics and ProbabilityGeneralizationPopulationBiology01 natural sciencesModels BiologicalGeneral Biochemistry Genetics and Molecular Biology010104 statistics & probability03 medical and health sciencesStatisticsEscherichia coliApplied mathematicsQuantitative Biology::Populations and EvolutionLimit (mathematics)0101 mathematicsSelection GeneticeducationSelection (genetic algorithm)education.field_of_studyFitness functionGeneral Immunology and MicrobiologyApplied MathematicsGeneral MedicineQuantitative Biology::GenomicsBiological Evolution030104 developmental biologyDistribution (mathematics)Modeling and SimulationMutation (genetic algorithm)MutationEpistasisGeneral Agricultural and Biological SciencesMathematical biosciences
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Variance component analysis to assess protein quantification in biomarker discovery. Application to MALDI-TOF mass spectrometry.

2017

International audience; Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. We propose a novel approach to variance component analysis with application to the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and use this approach for protein quantification by a classical signal processing algori…

0301 basic medicineStatistics and ProbabilityMALDI-TOFexperimental designBiometryprotein quantificationQuantitative proteomicsVariance component analysis[ CHIM ] Chemical Sciences01 natural sciencesSignaltechnological variability010104 statistics & probability03 medical and health sciencesstatistical analysis[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[CHIM.ANAL]Chemical Sciences/Analytical chemistryComponent (UML)[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]biomarker discoverysum of squares type0101 mathematicsBiomarker discoverysignal processingMathematicsSignal processingAnalysis of Variance[ PHYS ] Physics [physics]Noise (signal processing)ProteinsGeneral MedicineVariance (accounting)[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]030104 developmental biologySpectrometry Mass Matrix-Assisted Laser Desorption-IonizationLinear Modelsvariance components[ CHIM.ANAL ] Chemical Sciences/Analytical chemistryStatistics Probability and UncertaintyBiological systemAlgorithmsBiomarkersBiometrical journal. Biometrische Zeitschrift
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A heuristic, iterative algorithm for change-point detection in abrupt change models

2017

Change-point detection in abrupt change models is a very challenging research topic in many fields of both methodological and applied Statistics. Due to strong irregularities, discontinuity and non-smootheness, likelihood based procedures are awkward; for instance, usual optimization methods do not work, and grid search algorithms represent the most used approach for estimation. In this paper a heuristic, iterative algorithm for approximate maximum likelihood estimation is introduced for change-point detection in piecewise constant regression models. The algorithm is based on iterative fitting of simple linear models, and appears to extend easily to more general frameworks, such as models i…

0301 basic medicineStatistics and ProbabilityMathematical optimizationIterative methodHeuristic (computer science)Linear model01 natural sciencesPiecewise constant model Approximate maximum likelihood Model linearization Grid search limitations010104 statistics & probability03 medical and health sciencesComputational MathematicsDiscontinuity (linguistics)030104 developmental biologyHyperparameter optimizationCovariatePiecewise0101 mathematicsStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaChange detectionMathematics
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A graphical model selection tool for mixed models

2017

Model selection can be defined as the task of estimating the performance of different models in order to choose the most parsimonious one, among a potentially very large set of candidate statistical models. We propose a graphical representation to be considered as an extension to the class of mixed models of the deviance plot proposed in the literature within the framework of classical and generalized linear models. This graphical representation allows, once a reduced number of models have been selected, to identify important covariates focusing only on the fixed effects component, assuming the random part properly specified. Nevertheless, we suggest also a standalone figure representing th…

0301 basic medicineStatistics and ProbabilityMixed modelModel selectionFeature selection01 natural sciencesTask (project management)Deviance plot Penalized Weighted Residual Sum of Squares Variable selection010104 statistics & probability03 medical and health sciences030104 developmental biologyModeling and SimulationStatisticsGraphical model0101 mathematicsSelection (genetic algorithm)Mathematics
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Multiplicity- and dependency-adjusted p-values for control of the family-wise error rate

2016

Abstract Under the multiple testing framework, we propose the multiplicity- and dependency-adjustment method (MADAM) which transforms test statistics into adjusted p -values for control of the family-wise error rate. For demonstration, we apply the MADAM to data from a genetic association study.

0301 basic medicineStatistics and ProbabilityWord error rateMultiplicity (mathematics)Familywise error rateMadam01 natural sciences010104 statistics & probability03 medical and health sciences030104 developmental biologyStatisticsMultiple comparisons problemŠidák correctionPer-comparison error rate0101 mathematicsStatistics Probability and UncertaintyMathematicsStatistical hypothesis testingStatistics & Probability Letters
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Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications

2015

The bootstrap method has become a widely used tool applied in diverse areas where results based on asymptotic theory are scarce. It can be applied, for example, for assessing the variance of a statistic, a quantile of interest or for significance testing by resampling from the null hypothesis. Recently, some approaches have been proposed in the biometrical field where hypothesis testing or model selection is performed on a bootstrap sample as if it were the original sample. P-values computed from bootstrap samples have been used, for example, in the statistics and bioinformatics literature for ranking genes with respect to their differential expression, for estimating the variability of p-v…

0301 basic medicineStatistics and Probabilityeducation.field_of_studyComputer scienceModel selectionBootstrap aggregatingPopulationGeneral MedicineAsymptotic theory (statistics)01 natural sciences010104 statistics & probability03 medical and health sciences030104 developmental biologyResamplingStatisticsEconometrics0101 mathematicsStatistics Probability and UncertaintyeducationNull hypothesisQuantileStatistical hypothesis testingBiometrical Journal
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Collective Cell Migration in a Fibrous Environment: A Hybrid Multiscale Modelling Approach

2021

International audience; The specific structure of the extracellular matrix (ECM), and in particular the density and orientation of collagen fibres, plays an important role in the evolution of solid cancers. While many experimental studies discussed the role of ECM in individual and collective cell migration, there are still unanswered questions about the impact of nonlocal cell sensing of other cells on the overall shape of tumour aggregation and its migration type. There are also unanswered questions about the migration and spread of tumour that arises at the boundary between different tissues with different collagen fibre orientations. To address these questions, in this study we develop …

0301 basic medicineStatistics and Probabilitymulti-scale hybrid mathematical modelMaterials sciencecell migration[SDV.CAN]Life Sciences [q-bio]/Cancercontinuous cell-extracellular matrix interactionsQA273-280Articlenumerical simulationsExtracellular matrix03 medical and health sciences0302 clinical medicineCollagen fibres[SDV.BC.IC]Life Sciences [q-bio]/Cellular Biology/Cell Behavior [q-bio.CB][NLIN]Nonlinear Sciences [physics][MATH]Mathematics [math]T57-57.97Applied mathematics. Quantitative methodsApplied MathematicsCollective cell migrationCell migrationTumour invasionCollagen fibre030104 developmental biologyorientation of extracellular matrix fibresagent based discrete cell-cell interactionsContinuous fieldBiological systemProbabilities. Mathematical statistics030217 neurology & neurosurgeryFrontiers in Applied Mathematics and Statistics
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Kinematic Sub-Populations in Bull Spermatozoa: A Comparison of Classical and Bayesian Approaches

2020

The ejaculate is heterogenous and sperm sub-populations with different kinematic patterns can be identified in various species. Nevertheless, although these sub-populations are statistically well defined, the statistical differences are not always relevant. The aim of the present study was to characterize kinematic sub-populations in sperm from two bovine species, and diluted with different commercial extenders, and to determine the statistical relevance of sub-populations through Bayesian analysis. Semen from 10 bulls was evaluated after thawing. An ISAS&reg

0301 basic medicineendocrine systemMultivariate statisticsKinematicsCASABayesian probabilitySemenKinematicsBiologyArticleGeneral Biochemistry Genetics and Molecular Biologylaw.invention03 medical and health sciences0302 clinical medicinelawStatisticsbulllcsh:QH301-705.5030219 obstetrics & reproductive medicineGeneral Immunology and MicrobiologySub populationsurogenital systemExtenderMotilitySpermSpermatozoaBulls030104 developmental biologylcsh:Biology (General)ClusterPrincipal component analysisGeneral Agricultural and Biological Sciences
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Concise update on colorectal cancer epidemiology

2020

Colorectal cancer is a type of gastrointestinal malignancy originating from either the colon or rectum. In this short report we provide a concise update on recent colorectal cancer statistics, especially concerning frequency, mortality, life expectancy and risk factors. Overall, colorectal cancer is the third more frequent malignant disease around the world (1.85 million of new cases/years; 10.2% of total malignancies), with 2.27% cumulative risk of onset between 0–74 years. The age-standardized rate increases by over 10-fold before the age of 50 up to ≥85 years, whilst men have ~50% enhanced risk compared to women (the 0–74 years risk is 2.75% in men and 1.83% in women, respectively). Alth…

0301 basic medicinemedicine.medical_specialtyColorectal cancerRectumDistant CancerReview ArticleOverweight03 medical and health sciences0302 clinical medicineInternal medicineEpidemiologymedicineIn patientbusiness.industryDietary fibreColorectal cancer; epidemiology; frequency; mortality; statisticsGeneral Medicinemedicine.diseaseColorectal cancermortality030104 developmental biologymedicine.anatomical_structurestatisticsfrequency030220 oncology & carcinogenesisLife expectancyepidemiologymedicine.symptombusinessAnnals of Translational Medicine
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