Data set and variable selection
Variable Selection for Ultra High Dimensional Data
... big data problems with millions or more of ...model selection consistency, as the SIS ...the selection of ...accurate selection of true ...
130
Variable Selection when Confronted with Missing Data
... missing data method is complete case ...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 ...
220
Variable Selection and Prediction in Messy'' High-Dimensional Data"
... real data example, with the recently-proposed Convex Conditioned Lasso (CoCoLasso) as well as the naive Lasso which assumes that covariates are measured without ...of selection paths, we saw that MEBoost ...
99
Bayesian Biclustering on Discrete Data: Variable Selection Methods
... Chapter 1: Introduction space; etc. Subspace models are also called biclustering, or two-way clustering, which is the model this dissertation will explore in more detail in the following chapters. Clustering algorithms ...
142
Variable selection via Lasso with high-dimensional proteomic data
... response variable is sub-type of breast cancer given a certain ...proteomic data analyses is a "ratio-based" procedure, and the type of predictor variables are continuous while the type of response ...
46
Stable variable selection for right censored data: comparison of methods
... real data sets. The first data set concerns the survival of breast cancer patients in relation with their gene-expression signature [11] and is a very usual case encountered in survival ...second ...
29
A Sparse PLS for Variable Selection when Integrating Omics data
... Y data set contained 22 missing values. This data set is characterized by a very small number of samples ...Yeast data set Saccharomyces cerevisiae is an important component of ...
25
Shrinkage methods for variable selection and prediction with applications to genetic data
... In this thesis, we focus on the analysis of genetic data. However, the methods we discuss could be applied to any appropriate data set. Specifically in biology, technologies have been developed to ...
120
Robust Variable Selection
... from a heavy tailed distribution. In Table 2.2, we see that both of the model error ratios are similar across the five robust VAMS procedures. This is a result we expect, since many of these procedures are only slight ...
94
Data-adaptive Selection Of The Adjustment Set In Variable Importance Estimation
... a set of candidate explanatory variables, two-way interactions were explored based on repeated- measures regression models aimed at predicting Y as function of two candidate explanatory variables as well as the ...
28
Variable selection with Random Forests for missing data
... Its set- tings were chosen to fit ntree = 100 trees for each ...each selection step the number of surrogate splits was chosen to be maxi- mally maxsurrogate = ...Each variable was part of the ...
14
A Bayesian Variable Selection Method with Applications to Spatial Data
... other variable selection methods out there besides SSVS: Shrinkage based methods (Lasso, ridge), Bayesian Lasso, Adaptive Shrinkage ...other variable selection methods work and apply them on ...
48
Bridge Models and Variable Selection Methods for Spatial Data.
... pollutant data to generate responses, we evaluate our under the more realistic scenario in which covariates of interest are moderately to strongly correlated within site across ...we set ω k = ...pollution ...
106
Functional regression analysis and variable selection for motion data
... complex set-ups or calibrations before use every ...Each set of devices contains one base unit connected to the computer to gain power and collect signals from the ...each set of devices also ...
203
An application of Bayesian variable selection to international economic data
... In this paper, we will select m odels for predictin g a n a tio n ’s gross dom estic p rod u ct (G D P ) using a set o f candidate predictors. G D P plays an im portant role in p e o p le ’s lives. For exam ple, ...
39
Variable selection optimization for multivariate classification of metabolomics data
... One could think that, because the number of retained variables in the EDF stage of CARS decreases for each MCS run, in addition to the random component of the ARS stage, repeating a training set between runs ...
97
Classification and Variable Selection Methods for Ultrahigh Dimensional and Imbalanced Data.
... and variable selection are hot topics in machine learning and statistical ...a set of categories a new observation belongs, based on a training set of data containing observations whose ...
88
Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy
... response variable indicating bankruptcy is dichotomous, we nonethe- less fit a linear model via least ...the data (bankruptcy is ...our data, however, it is hard to expect a statistical model to ...
36
Bayesian variable selection and modelling for metastatic breast cancer data
... model selection procedure is applied to data on 90 women with metastatic breast ...able selection procedure. The model space is reduced using posterior variable inclusion probabilities, and ...
14
Kernel-Based Data Mining Approach with Variable Selection for Nonlinear High-Dimensional Data
... best variable sets but the existing algorithm required a longer computational time than the modified one, particularly at the longer cycle ...is set to the previous ...
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