Data analysis and variable selection
Variable selection in robust joint mean and covariance model for longitudinal data analysis
... longitudinal data analysis, a correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression ...robust variable selection method in a joint ...
18
Variable selection for models with missing data
... Melanoma data analyses. The results of the Melanoma data analysis indicate that when predictors are not strongly significant, the results from penalized likelihood maximization may differ depending ...
116
Variable selection with incomplete covariate data.
... We introduced new criteria for model selection in presence of missing data, through the utilization of the EM algorithm and the weighting method of Ibrahim (1990). The new criteria are immediately obtained ...
22
Variable selection via Lasso with high-dimensional proteomic data
... high-dimensional data analysis, we have data sets where the number of variables exceeds the number of ...response variable, and suppose the Lasso solution β ˆ at a certain λ is ( ˆ β 1 , β ˆ 2 ...
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Joint Variable Selection of Mean Covariance Model for Longitudinal Data
... model selection for joint mean-covariance analysis based ...BIC selection method would suffer from expensive computational ...longitudinal data, that implies that our method can avoid the ...
9
Classification and Variable Selection Methods for Ultrahigh Dimensional and Imbalanced Data.
... dimensional data analysis, which is a great start in my PhD ...quantitative analysis in the financial ...series analysis skills, but also the passion and strictness for the ...
88
Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy
... Large data sets and inexpensive computing are symbols of our ...the data-mining community have enthusiastically embraced this ...careful data analysis while warning of parameter bias and other ...
36
Variable selection in model-based clustering for high-dimensional data
... ”functional data analysis” to the analysis of such ...tional data often arise from measurements on fine time grids, and if the sampling grid is sufficiently dense, the resulting data ...
274
Bayesian variable selection and modelling for metastatic breast cancer data
... the data analysis as discussed by Mallett et ...the data. We also divided the data into eight data sets based on cell locations and tissue types to allow us to identify the different ...
14
Improving cluster analysis with automatic variable selection based on trees
... of data analysis, implement the more formal technique of ...of data analytics face the challenge of turning an abundance of information into useful tools that can assist in making critical ...of ...
62
Functional regression analysis and variable selection for motion data
... dom variable. The idea is to represent the functional variable by a set of orthogonal basis functions estimated from the auto-covariance ...functional data and dependent samples, ...observed ...
203
Latent class analysis variable selection
... search variable selection and clus- tering algorithm for the case of discrete data modeled by conditionally independent multi- nomially distributed ...
33
Variable Selection for Market Basket Analysis
... basket data and study cross category eects have been developed in the elds of statistics, data mining, and marketing ...basket data acquired by conventional and electronic retailers, loyalty card ...
20
Variable Selection when Confronted with Missing Data
... missing data method is complete case ...the data set. This solves the problem of how to handle those cases where data are missing, but can lead to substantial bias in any resulting inference because ...
220
Bayesian Variable Selection with Applications to Neuroimaging Data
... the analysis of high- dimensional neuroimaging ...EEG data, where we have a matrix of covariates corresponding to each subject from either the alcoholic or control ...inherent variable ...
151
Variable selection with Random Forests for missing data
... forests, variable selection, missing data, multiple imputation, surrogates, complete case analysis 1 Introduction Random forests (Breiman, 2001) are appreciated in many research fields for ...
14
Variable selection in model-based discriminant analysis
... our variable selection procedure. First, the interest of variable selection for non linear discriminant analysis mod- els such as QDA is highlighted on simulated ...real data ...
35
Genome wide association analysis of GAW17 data using an empirical Bayes variable selection
... a variable selection property that assumes that not all SNPs in a genetic region contribute to a gene-based ...the variable selection property of POCRE might rule out true causal SNPs in the ...
5
A Bayesian Variable Selection Method with Applications to Spatial Data
... FERT3 0.1753409 0.3377581 0.4879755 PH1 0.3106712 0.4437057 0.5444190 4.2.3 Spatial Model Building In the previous section, we applied SSVS method in identifying the important covariates. In this section, we will use ...
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Variable Selection and Prediction in Messy'' High-Dimensional Data"
... external data sources, but absent such data one could perform a sen- sitivity analysis with different measurement error variances and correlation structures, as we demonstrate in the real data ...
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