Search results for " missing data"

showing 5 items of 5 documents

cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values

2023

Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse co…

Statistics and Probabilityconditional Gaussian graphical modelscglasso conditional Gaussian graphical models glasso high-dimensionality sparsity censoring missing dataglassosparsityhigh-dimensionalityconditional Gaussian graphical models glasso high-dimensionality sparsity censoring missing datacglassomissing datacensoringStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaSoftware
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WEIGHTS AND IMPUTATIONS

2019

This chapter provides a description of the weighting and imputation strategies used to address problems of unit nonresponse, sample attrition and item nonresponse in the seventh wave of SHARE.

Settore SECS-P/05 - EconometriaWeights Imputations nonresponse errors attrition missing data.
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Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach

2021

Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to generality of do-calculus, the search is capable of taking more advanced data-generating mechanisms into account along with an arbitrary type of both observational and…

FOS: Computer and information sciencesStatistics and ProbabilityComputer Science - Machine LearningcausalityComputer Science - Artificial IntelligenceHeuristic (computer science)Computer scienceeducationMachine Learning (stat.ML)transportabilitycomputer.software_genre01 natural sciencesMachine Learning (cs.LG)R-kielimissing dataQA76.75-76.765; QA273-280010104 statistics & probabilitydo-calculuscausality; do-calculus; selection bias; transportability; missing data; case-control design; meta-analysisStatistics - Machine LearningSearch algorithmselection bias0101 mathematicsParametric statisticspäättelymeta-analyysicase-control designhakualgoritmit113 Computer and information sciencesMissing datameta-analysisIdentification (information)Artificial Intelligence (cs.AI)Causal inferencekausaliteettiIdentifiabilityProbability distributionData miningStatistics Probability and UncertaintycomputerSoftwareJournal of Statistical Software
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Empirical Orthogonal Function and Functional Data Analysis Procedures to Impute Long Gaps in Environmental Data

2016

Air pollution data sets are usually spatio-temporal multivariate data related to time series of different pollutants recorded by a monitoring network. To improve the estimate of functional data when missing values, and mainly long gaps, are present in the original data set, some procedures are here proposed considering jointly Functional Data Analysis and Empirical Orthogonal Function approaches. In order to compare and validate the proposed procedures, a simulation plan is carried out and some performance indicators are computed. The obtained results show that one of the proposed procedures works better than the others, providing a better reconstruction especially in presence of long gaps.

Multivariate statisticsComputer scienceFunctional data analysisEmpirical orthogonal functionsMissing datacomputer.software_genreEnvironmental dataEOF FDA Missing data Environmental dataSet (abstract data type)Singular value decompositionPerformance indicatorData miningSettore SECS-S/01 - Statisticacomputer
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EOFs for gap filling in multivariate air quality data: a FDA approach

2010

Missing values are a common concern in spatiotemporal data sets. During recent years a great number of methods have been developed for gap filling. One of the emerging approaches is based on the Empirical Orthogonal Function (EOF) methodology, applied mainly on raw and univariate data sets presenting irregular missing patterns. In this paper EOF is carried out on a multivariate space-time data set, related to concentrations of pollutants recorded at different sites, after denoising raw data by FDA approach. Some performance indicators are computed on simulated incomplete data sets with also long gaps in order to show that the EOF reconstruction appears to be an improved procedure especially…

Settore SECS-S/01 - StatisticaFDA EOF missing data gap filling
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