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[PDF] Top 20 Effect of missing data on multitask prediction methods

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Effect of missing data on multitask prediction methods

Effect of missing data on multitask prediction methods

... test prediction performance with respect to increasing data sparsity, we needed complete data sets where compounds had been tested consistently across a set of ...two data sets: the PKIS ... See full document

13

A comparison of methods to estimate the survivor average causal effect in the presence of missing data: a simulation study

A comparison of methods to estimate the survivor average causal effect in the presence of missing data: a simulation study

... where data is not available for Table 2 Standardised difference between exposure groups for 20,321 participants of the Melbourne Collaborative Cohort Study with non-missing data on age-related ... See full document

14

Confounding and missing data in cost-effectiveness analysis: comparing different methods

Confounding and missing data in cost-effectiveness analysis: comparing different methods

... Both methods based on the regression models could have been applied in the case of repeated measurements by using either frequentist or Bayesian ...the missing data values were augmented during the ... See full document

11

Imputed Empirical Likelihood for Varying Coefficient Models with Missing Covariates

Imputed Empirical Likelihood for Varying Coefficient Models with Missing Covariates

... The empirical likelihood-based inference for varying coefficient models with missing covariates is investigated. An imputed empirical likelihood ratio function for the coefficient functions is proposed, and it is ... See full document

5

Statistical Methods for Non-Ignorable Missing Data With Applications to Quality-of-Life Data.

Statistical Methods for Non-Ignorable Missing Data With Applications to Quality-of-Life Data.

... Pseudo-likelihood methods (Gong and Samaniego, 1981; Parke, 1986) and composite marginal likelihood methods (Cox and Reid, 2004; Varin et ...pseudo-likelihood methods can be viewed as an extension of ... See full document

124

Comparative Analysis of Multi-fractal Data Missing Processing Methods

Comparative Analysis of Multi-fractal Data Missing Processing Methods

... Abstract: Data missing often affects the characteristics of the ...appropriate methods to process the missing data is the premise and guarantee to obtain high quality ...the ... See full document

6

An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data

An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data

... 36 data sets as in Perlich, Provost, and Simonoff (2003) are used here (except for Cover- type and Patent, which are too big for RPART to handle; in those cases a random subset of 100,000 observations for each of ... See full document

40

Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes

Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes

... for missing data. We used Bayesian methods to fit hierarchical models accounting for different sources of uncertainty, to evaluate the as- sociation of geographical covariates with DM II ... See full document

13

Spectral methods for imputation of missing air quality data

Spectral methods for imputation of missing air quality data

... air-quality data imputa- tion, which uses information from the air monitoring stations array is Site-Dependent Effect Method (SDEM) (Plaia and Bondi ...The missing data is then imputed by ... See full document

13

A comparison of multiple imputation methods for missing data in longitudinal studies

A comparison of multiple imputation methods for missing data in longitudinal studies

... JM-MLMM-LN methods for imputing in- complete binary and continuous data in the context of in- dividual patient data meta-analysis, and found that JM-MLMM-LN performed better than FCS-LMM-het and ... See full document

16

Statistical analysis and handling of missing data in cluster randomized trials: a systematic review

Statistical analysis and handling of missing data in cluster randomized trials: a systematic review

... independent data point [20, ...dependent data, reducing all observations within a cluster to a single summary measure decreases the sample size and ...design effect, an inflation factor used to ... See full document

10

Comparison of Methods for Processing Missing Values in Large Sample Survey Data

Comparison of Methods for Processing Missing Values in Large Sample Survey Data

... three methods processing survey data with missing value at different missing rates are ...original data set K, the higher percentage of missing data, the more significant ... See full document

8

From Predictive Methods to Missing Data Imputation: An Optimization Approach

From Predictive Methods to Missing Data Imputation: An Optimization Approach

... the effect of each individual method from the cross-validation procedure, we further summarize the results by comparing one method at a time against the benchmark ...of methods. When comparing opt.cv ... See full document

39

Comparison of Four Methods for Handing Missing Data in Longitudinal Data Analysis through a Simulation Study

Comparison of Four Methods for Handing Missing Data in Longitudinal Data Analysis through a Simulation Study

... with missing values. These appropriate methods for analysis should be selected based on the data missing mechanism, since different statistical methods are valid only under certain ... See full document

13

“Philosophical Treatises on Life and Death”:Newspaper Coverage of a Controversial Brain Death Case

“Philosophical Treatises on Life and Death”:Newspaper Coverage of a Controversial Brain Death Case

... As pesticides are widely used in agriculture, more and more people who work at places like farm are exposed to the pesticides. According to enviroment re- searches [Villarejo; 2003; Reigart and Roberts; 1999], being ... See full document

65

SOFTWARE EFFORT PREDICTION: AN EMPIRICAL EVALUATION OF METHODS TO TREAT MISSING VALUES WITH RAPIDMINER

SOFTWARE EFFORT PREDICTION: AN EMPIRICAL EVALUATION OF METHODS TO TREAT MISSING VALUES WITH RAPIDMINER

... from data after applying the three different methods to treat missing ...to data as well as the predictive capability of the induced ... See full document

9

Comparison of the Methods to Estimate Missing Values in Monthly Precipitation Data

Comparison of the Methods to Estimate Missing Values in Monthly Precipitation Data

... Researchers have widely studied issues on climate change. One of the essential indicators of climate change is rainfall pattern. Rainfall patterns changes affect the humidity and aridity events of an area. These can ... See full document

7

Diabetes data prediction using data classification algorithm

Diabetes data prediction using data classification algorithm

... The target of the information extracting method is to extract data from a dataset and make over it into a clear construction for additional use. This is a diagnostic method planned to scrutinized the information ... See full document

5

Task Clustering and Gating for Bayesian Multitask Learning

Task Clustering and Gating for Bayesian Multitask Learning

... newspaper data, we also look at non-Bayesian multitask learning: in this intermediate model we applied the same network structure as in the Bayesian multitask learning model, yet instead of ... See full document

17

When is multitask learning effective? Semantic sequence prediction under varying data conditions

When is multitask learning effective? Semantic sequence prediction under varying data conditions

... Multitask learning has been applied suc- cessfully to a range of tasks, mostly mor- phosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to deter- ... See full document

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