Search results for "Statistics & Probability"
showing 10 items of 436 documents
Incidence and control of black spot syndrome of tiger nut
2017
Tiger nut (Cyperus esculentum) is a very profitable crop in Valencia, Spain, but in the last years, part of the harvested tubers presents black spots in the skin making them unmarketable. Surveys performed in two consecutive years showed that about 10% of the tubers were severely affected by the black spot syndrome whose aetiology is unknown. Disease control procedures based on selection of tubers used as seed (seed tubers) or treatment with hot-water and/or chemicals were assayed in greenhouse. These assays showed that that this syndrome had a negative impact on the germination rate, tuber size and yield. Selection of asymptomatic seed tubers reduced drastically the incidence of the black …
Adaptation, coordination, and local interactions via distributed approachability
2017
This paper investigates the relation between cooperation, competition, and local interactions in large distributed multi-agent\ud systems. The main contribution is the game-theoretic problem formulation and solution approach based on the new framework\ud of distributed approachability, and the study of the convergence properties of the resulting game model. Approachability\ud theory is the theory of two-player repeated games with vector payoffs, and distributed approachability is here presented for\ud the first time as an extension to the case where we have a team of agents cooperating against a team of adversaries under local\ud information and interaction structure. The game model turns i…
VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS
2014
International audience; The paper proposes a variable selection method for pro-teomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (…
Diagramme mit ggplot2
2020
Mit dem Zusatzpaket ggplot2 lassen sich die in Kap. 14 vorgestellten Diagrammtypen ebenfalls erstellen. Dabei ist die Herangehensweise eine grundsatzlich andere: Wahrend der Basisumfang von R fur verschiedene Diagrammarten einzelne Funktionen bereitstellt, werden mit ggplot2 alle Diagrammtypen mit einem einheitlichen System erzeugt. Sind Diagramme des Basisumfangs analog zu einer Leinwand, auf der jede Funktion spater nicht mehr anderbare Elemente aufmalt, reprasentiert ggplot2 alle Diagrammelemente explizit in einem Objekt. Erstellte Diagramme lassen sich uber dieses Objekt weiter verandern, an Funktionen ubergeben und speichern.
Reducing sample size in experiments with animals: historical controls and related strategies
2015
Reducing the number of animal subjects used in biomedical experiments is desirable for ethical and practical reasons. Previous reviews of the benefits of reducing sample sizes have focused on improving experimental designs and methods of statistical analysis, but reducing the size of control groups has been considered rarely. We discuss how the number of current control animals can be reduced, without loss of statistical power, by incorporating information from historical controls, i.e. subjects used as controls in similar previous experiments. Using example data from published reports, we describe how to incorporate information from historical controls under a range of assumptions that mig…
A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data
2021
Growing interest in understanding microbiota dynamics has motivated the development of different strategies to model microbiota time series data. However, all of them must tackle the fact that the available data are high-dimensional, posing strong statistical and computational challenges. In order to address this challenge, we propose a Dirichlet autoregressive model with time-varying parameters, which can be directly adapted to explain the effect of groups of taxa, thus reducing the number of parameters estimated by maximum likelihood. A strategy has been implemented which speeds up this estimation. The usefulness of the proposed model is illustrated by application to a case study.
Toward a direct and scalable identification of reduced models for categorical processes.
2017
The applicability of many computational approaches is dwelling on the identification of reduced models defined on a small set of collective variables (colvars). A methodology for scalable probability-preserving identification of reduced models and colvars directly from the data is derived—not relying on the availability of the full relation matrices at any stage of the resulting algorithm, allowing for a robust quantification of reduced model uncertainty and allowing us to impose a priori available physical information. We show two applications of the methodology: (i) to obtain a reduced dynamical model for a polypeptide dynamics in water and (ii) to identify diagnostic rules from a standar…
Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling
2016
Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on …
Melanoma-Nevus Discrimination Based on Image Statistics in Few Spectral Channels
2016
The purpose of this paper is to offer a method for discrimination of cutaneous melanoma from benign nevus, founded on analysis of skin lesion image. At the core of method is calculation of mean and standard deviation of pixel optical density values for a few narrow spectral bands. Calculated values are compared with discriminating thresholds derived from a set of images of benign nevi and melanomas with known diagnosis. Classification is done applying weighted majority rule to results of thresholding. Verification against the available multispectral images of 32 melanomas and 94 benign nevi has shown that the method using three spectral bands provided zero false negative and four false posi…
Two-Stage Bayesian Approach for GWAS With Known Genealogy
2019
Genome-wide association studies (GWAS) aim to assess relationships between single nucleotide polymorphisms (SNPs) and diseases. They are one of the most popular problems in genetics, and have some peculiarities given the large number of SNPs compared to the number of subjects in the study. Individuals might not be independent, especially in animal breeding studies or genetic diseases in isolated populations with highly inbred individuals. We propose a family-based GWAS model in a two-stage approach comprising a dimension reduction and a subsequent model selection. The first stage, in which the genetic relatedness between the subjects is taken into account, selects the promising SNPs. The se…