Consulta: subjectFacets:"Missing data"
Registros recuperados: 15
Data/hora: 05/06/2024 14:30:37

Modelo de espacio de estados con observaciones censuradas.
Provedor de dados: 32
Autores: Ariza Hernández, Francisco Julián.
En este trabajo, presentamos algunas alternativas par estimar los parámetros de un Modelo de Espacio de Estados (SSM, por sus siglas en inglés) cuando se tiene el problema de datos incompletos, los algoritmos de Esperanza-Maximización (EM), Monte Carlo EM y EM Estocástico son implementados. También, se presenta una aproximación a la función de verosimilitud utilizando Muestreo de Importancia. Se realizó un estudio de simulación para estudiar el desempeño de estos procedimientos para un modelo de espacio de estados con diferentes porcentajes de censura en las observaciones. Los algoritmos son implementados a dos conjuntos de datos reales; el primero, a datos sobre contaminación del aire con observaciones sujetas a límites inferiores de detección y con datos...
Palavras-chave: Algoritmo EM; Algoritmo EM estocástico; Algoritmo EM Monte Carlo; Recursiones de Kalman; Límites de detección; Datos perdidos; EM algorithm; Stochastic EM algorithm; Monte Carlo EM algorithm; Kalman recursions; Limits of detection; Missing data; Doctorado; Estadística.
Ano: 2010
URL: http://hdl.handle.net/10521/302

Which DTW Method Applied to Marine Univariate Time Series Imputation
Provedor de dados: 5
Autores: Phan, Thi-thu-hong; Poisson-caillault, Emilie; Lefebvre, Alain; Bigand, Andre.
Missing data are ubiquitous in any domains of applied sciences. Processing datasets containing missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Therefore, the aim of this paper is to build a framework for filling missing values in univariate time series and to perform a comparison of different similarity metrics used for the imputation task. This allows to suggest the most suitable methods for the imputation of marine univariate time series. In the first step, the missing data are completed on various mono-dimensional time series. To fill a missing sub-sequence (gap) in a time series, we first find the most similar sub-sequence to the sub-sequence before (resp. after) this gap according a...
Tipo: Text
Palavras-chave: Univariate time series; Missing data; Dynamic Time Warping (DTW); Derivative DTW (DDTW); Dynamic Time Warping-D (DTW-D); Adaptive Feature Based DTW (AF-BDTW); Similarity measures.
Ano: 2017
URL: http://archimer.ifremer.fr/doc/00435/54681/56096.pdf

DTW-Approach for uncorrelated multivariate time series imputation
Provedor de dados: 5
Autores: Phan, Thi-thu-hong; Poisson Caillault, Emilie; Bigand, Andre; Lefebvre, Alain.
Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper, we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of recurrent data. This method involves two main steps. Firstly, we find the most similar sub-sequence to the sub-sequence before (resp.after) a gap based on the shape-features extraction and Dynamic Time Warping algorithms. Secondly, we fill in the gap by the next (resp.previous) sub-sequence...
Tipo: Text
Palavras-chave: Imputation; Uncorrelated multivariate time series; Missing data; Dynamic Time Warping; Similarity measures.
Ano: 2017
URL: https://archimer.ifremer.fr/doc/00429/54082/55378.pdf

Time series analysis of interrupted long-term data set (1961-1991) of zooplankton abundance in Gulf of Maine (northern Atlantic, USA)
Provedor de dados: 5
Autores: Licandro, P; Conversi, A; Ibanez, F; Jossi, J.
The main interannual and seasonal signals have been extracted from a multi-decadal data set of zooplankton collected with the Continuous Plankton Recorder in the Gulf of Maine, from January 1961 to December 1991. The monthly abundances of seven species or genera of copepods representing the dominant biomass in the area were considered. The presence of a large consecutive gap (35 months) prevented the use of statistical methods for the prediction of missing data. The eigen-vector filtering (EVF) method was then used on the original time series, while retaining the missing values. For each zooplankton taxon, two principal modes of variability (F-1 and F-2) were extracted, representing the interannual and seasonal variations, respectively. Results of EVF...
Tipo: Text
Palavras-chave: Calanus finmarchicus; Golfe du Maine; Données manquantes; Séries temporelles; Zooplancton; Calanus finmarchicus; Gulf of Maine; Missing data; Time series analysis; Zooplankton.
Ano: 2001
URL: http://archimer.ifremer.fr/doc/00322/43342/42885.pdf

Dynamic Time Warping-based imputation for univariate time series data
Provedor de dados: 5
Autores: Phan, Thi-thu-hong; Poisson Caillault, Émilie Poisson; Lefebvre, Alain; Bigand, André.
Time series with missing values occur in almost any domain of applied sciences. Ignoring missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). This paper proposes an approach to fill in large gap(s) within time series data under the assumption of effective information. To obtain the imputation of missing values, we find the most similar sub-sequence to the sub-sequence before (resp. after) the missing values, then complete the gap by the next (resp. previous) sub-sequence of the most similar one. Dynamic Time Warping algorithm is applied to compare sub-sequences, and combined with the shape-feature extraction algorithm for reducing insignificant solutions. Eight well-known and real-world data...
Tipo: Text
Palavras-chave: Imputation; Missing data; Univariate time series; DTW; Similarity.
Ano: 2017
URL: http://archimer.ifremer.fr/doc/00396/50696/51387.pdf

A weighted AMMI algorithm for nonreplicated data
Provedor de dados: 86
Autores: Assis,Tatiana Oliveira Gonçalves de; Dias,Carlos Tadeu dos Santos; Rodrigues,Paulo Canas.
Abstract: The objective of this work was to propose a weighting scheme for the additive main effects and multiplicative interactions (AMMI) model, as well as to assess the usefulness of this W-AMMI model in the study of genotype x environment interaction (GxE) and quantitative trait locus x environment interaction (QxE) for nonreplicated data. Data from the 'Harrington' x TR306 barley (Hordeum vulgare) mapping population, with 141 genotypes evaluated in 25 environments, were used to compare the results from the AMMI model with those of two proposed versions of the W-AMMI model: equal weights per row and equal weights per column. The proposed W-AMMI columns algorithm is viable to analyze data with heterogeneous variance, when there are no replicates...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Hordeum vulgare; Contaminated data; Genotype-by-environment interaction; Missing data; Outliers; QTL detection.
Ano: 2018
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2018000500557

Tools for analyzing multiple imputed datasets
Provedor de dados: 31
Autores: Carlin,   John B.; Li,   Ning; Greenwood,   Philip; Coffey,   Carolyn.
The method of multiple imputation (MI) is used increasingly for analyzing datasets with missing observations. Two sets of tasks are required in order to implement the method: (a) generating multiple complete datasets in which missing values have been imputed by simulating from an appropriate probability distribution and (b) analyzing the multiple imputed datasets and combining complete data inferences from them to form an overall inference for parameters of interest. An increasing number of software tools are available for task (a), although this is difficult to automate, because the method of imputation should depend on the context and available covariate data. When the quantity of missing data is not great, the sensitivity of results to the imputation...
Tipo: Journal Article
Palavras-chave: Missing data; Multiple imputation; Rubin's rule of combination; Overall estimates; Research Methods/ Statistical Methods.
Ano: 2003
URL: http://purl.umn.edu/116087

Multiple imputation of missing values
Provedor de dados: 31
Autores: Royston,   Patrick.
Following the seminal publications of Rubin about thirty years ago, statisticians have become increasingly aware of the inadequacy of “complete-case” analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates, and a complete-case analysis may omit as many as half of the available cases. Hotdeck imputation was implemented in Stata in 1999 by Mander and Clayton. However, this technique may perform poorly when many rows of data have at least one missing value. This article describes an implementation for Stata of the MICE method of multiple multivariate imputation described by van Buuren, Boshuizen, and Knook (1999). MICE stands for multivariate imputation by chained...
Tipo: Journal Article
Palavras-chave: Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.
Ano: 2004
URL: http://purl.umn.edu/116244

A new framework for managing and analyzing multiply imputed data in Stata
Provedor de dados: 31
Autores: Carlin,   John B.; Galati,   John C.; Royston,   Patrick.
A new set of tools is described for performing analyses of an ensemble of datasets that includes multiple copies of the original data with imputations of missing values, as required for the method of multiple imputation. The tools replace those originally developed by the authors. They are based on a simple data management paradigm in which the imputed datasets are all stored along with the original data in a single dataset with a vertically stacked format, as proposed by Royston in his ice and micombine commands. Stacking into a single dataset simplifies the management of the imputed datasets compared with storing them individually. Analysis and manipulation of the stacked datasets is performed with a new prefix command, mim, which can accommodate data...
Tipo: Article
Palavras-chave: Mim; Mimstack; Ice; Micombine; Miset; Mifit; Multiple imputation; Missing data; Missing at random; Research Methods/ Statistical Methods.
Ano: 2008
URL: http://purl.umn.edu/120928

Multiple imputation of missing values: update
Provedor de dados: 31
Autores: Royston,   Patrick.
This article describes a substantial update to mvis, which brings it more closely in line with the feature set of S. van Buuren and C. G. M. Oudshoorn’s implementation of the MICE system in R and S-PLUS (for details, see http://www.multiple-imputation.com). To make a clear distinction from mvis, the principal program of the new Stata release is called ice. I will give details of how to use the new features and a practical illustrative example using real data. All the facilities of mvis are retained by ice. Some improvements to micombine for computing estimates from multiply imputed datasets are also described.
Tipo: Journal Article
Palavras-chave: Ice; Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.
Ano: 2005
URL: http://purl.umn.edu/117511

Meta-analysis with missing data
Provedor de dados: 31
Autores: White,   Ian R.; Higgins,   Julian P.T.
A new command, metamiss, performs meta-analysis with binary outcomes when some or all studies have missing data. Missing values can be imputed as successes, as failures, according to observed event rates, or by a combination of these according to reported reasons for the data being missing. Alternatively, the user can specify the value of, or a prior distribution for, the informative missingness odds ratio.
Tipo: Article
Palavras-chave: Metamiss; Meta-analysis; Missing data; Informative missingness odds ratio; Research Methods/ Statistical Methods.
Ano: 2009
URL: http://purl.umn.edu/122700

Performance of different methods for reference evapotranspiration estimation in Jaíba, Brazil
Provedor de dados: 91
Autores: Silva,Gustavo H. da; Dias,Santos H. B.; Ferreira,Lucas B.; Santos,Jannaylton É. O.; Cunha,Fernando F. da.
ABSTRACT FAO Penman-Monteith (FO-PM) is considered the standard method for the estimation of reference evapotranspiration (ET0) but requires various meteorological data, which are often not available. The objective of this work was to evaluate the performance of the FAO-PM method with limited meteorological data and other methods as alternatives to estimate ET0 in Jaíba-MG. The study used daily meteorological data from 2007 to 2016 of the National Institute of Meteorology’s station. Daily ET0 values were randomized, and 70% of these were used to determine the calibration parameters of the ET0 for the equations of each method under study. The remaining data were used to test the calibration against the standard method. Performance evaluation was based on...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Missing data; Modeling; Irrigation management; FAO-Penman-Monteith.
Ano: 2018
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662018000200083

CALIBRATION OF THE HARGREAVES-SAMANI EQUATION FOR SPECIFIC PERIODS OF THE YEAR IN THE MUNICIPALITY OF JAÍBA-MG, BRAZIL
Provedor de dados: 107
Autores: Duarte, Anunciene Barbosa; Ferreira, Lucas Borges; dos Santos, Edson Fagne.
Reference evapotranspiration (ET0) explains the climatic effects on crop water demand. The Food and Agriculture Organization (FAO) recommends the Penman Monteith equation as a standard method for estimating ET0. However, because this equation requires a large amount of meteorological data, it has limited application. An alternative is the Hargreaves-Samani (HS) equation, which only requires air temperature data, and can be calibrated to specifc locations and periods. The present study aimed to calibrate the empirical parameters (coeffcients and exponent) of the HS equation for specifc periods of the year, as well as evaluate the behavior and calibration of this equation throughout the year in the municipality of Jaíba-MG, Brazil. The daily meteorological...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Metereologia Agrícola evapotranspiration; Irrigation scheduling; Missing data; Temperature.
Ano: 2017
URL: http://www.seer.ufv.br/seer/index.php/reveng/article/view/838

Distribution-free multiple imputation in an interaction matrix through singular value decomposition
Provedor de dados: 63
Autores: Bergamo,Genevile Carife; Dias,Carlos Tadeu dos Santos; Krzanowski,Wojtek Janusz.
Some techniques of multivariate statistical analysis can only be conducted on a complete data matrix, but the process of data collection often misses some elements. Imputation is a technique by which the missing elements are replaced by plausible values, so that a valid analysis can be performed on the completed data set. A multiple imputation method is proposed based on a modification to the singular value decomposition (SVD) method for single imputation, developed by Krzanowski. The method was evaluated on a genotype × environment (G × E) interaction matrix obtained from a randomized blocks experiment on Eucalyptus grandis grown in multienvironments. Values of E. grandis heights in the G × E complete interaction matrix were deleted randomly at three...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Missing data; Nonparametric; Eigenvalue; Eigenvector; Genotype-environment.
Ano: 2008
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162008000400015

Recovering missing data: estimating position and size of caudal vertebrae in Staurikosaurus pricei Colbert, 1970
Provedor de dados: 42
Autores: Grillo,Orlando N.; Azevedo,Sergio A.K.
Missing data is a common problem in paleontology. It makes it difficult to reconstruct extinct taxa accurately and restrains the inclusion of some taxa on comparative and biomechanical studies. Particularly, estimating the position of vertebrae on incomplete series is often non-empirical and does not allow precise estimation of missing parts. In this work we present a method for calculating the position of preserved middle sequences of caudal vertebrae in the saurischian dinosaur Staurikosaurus pricei, based on the length and height of preserved anterior and posterior caudal vertebral centra. Regression equations were used to estimate these dimensions for middle vertebrae and, consequently, to assess the position of the preserved middle sequences. It also...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Missing data; Caudal vertebra; Regression; Staurikosaurus pricei.
Ano: 2011
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652011000100004