# missing at random

### Optimal design when outcome values are not missing at random

**missing**values complicates statistical ...experiments,

**missing**values are particularly problematic when con- structing optimal designs, as it is not known which values are

**missing**at the design ...

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### Imputation of continuous variables missing at random using the method of simulated scores

**missing**data imply loss of information, so estimates will be less efficient than ...by

**missing**values are not readily ...with

**missing**data (Little and Rubin, ...where

**missing**values ...

7

### Adjusting HIV prevalence estimates for non participation: an application to demographic surveillance

**missing**data is only appropriate where the data are assumed to be either miss- ing completely at

**random**(MCAR; absence from the data does not depend on either observed or unobserved characteristics of ...

12

### Optical Properties of In1 xGaxN Epilayers Grown by HPCVD

**missing**data, and no research to date has investigated the impact of

**missing**data in the area of cross-classified ...handling

**missing**data, listwise deletion ...handling

**missing**data. While ...

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### From Predictive Methods to Missing Data Imputation: An Optimization Approach

**missing**data with mixed continuous and categorical ...of

**missing**at

**random**mechanisms and various

**missing**percentages, ...data

**missing**, the average out-of-sample R 2 is ...

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### Machine Learning for Adaptive Many Core Machines A Practical Approach (Studies in Big Data) 2015th Edition pdf

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### The effects of reducing worry in patients with persecutory delusions: study protocol for a randomized controlled trial

**Random**effects regression models will be fitted to the repeated measures to estimate treatment effects for outcomes, controlling for treatment center, in-patient status and the corresponding baseline ...

7

### Adjusting HIV prevalence estimates for non-participation: an application to demographic surveillance

12

### Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study

**missing**at

**random**(MAR) mechanism. Five

**missing**data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) ...

10

### Predictors of mental health help seeking among Polish people living in the United Kingdom

**missing**at

**random**rather than completely at

**random**, however the distinction between data

**missing**at

**random**(MAR) and not at

**random**(MNAR) could not be ...

12

### Tuning multiple imputation by predictive mean matching and local residual draws

13

### Sensitivity models for missing covariates in the analysis of survival data from multiple surveys

**missing**at

**random**and in which each of the binary predictor variables are mod- elled as independent Bernoulli

**random**variables; a model in which the covariates are modelled by a conditional wise ...

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### Analysis of generalized nonlinear structural equation models by using Bayesian approach with application

**missing**continuous and dichotomous data that involve data that are

**missing**at

**random**using maximum likelihood approach, as well as, he demonstrated the newly developed methods for estimation and ...

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### Estimation in semiparametric models with missing data

**missing**propensity function proposed by James Robins and colleagues, see for instance Robins, Rotnitzky and Zhao ...the

**missing**propensity function or the

**missing**at

**random**mechanism ...

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### Missing data problem in random electrocardiogram signal processing

**random**numbers were generated in 2000 terms at six significant ...of

**missing**data that was studied is the not

**missing**at

**random**(NMAR) ...not

**random**, the missingness mechanism ...

26

### Optimization with missing data

**missing**data is ignored a large number of updates are based on

**random**sampling and thus the failure rate is similar to the values in column ...the

**missing**data proves to be no better than a ...

11

### Investigating a rare methicillin-resistant <em>Staphylococcus aureus</em> strain: first description of genome sequencing and molecular characterization of CC15-MRSA

**missing**in RUH-2, while the coverage of hsdM- beta and the 5’-end of hsdS-beta is doubled with respect to other chromosomal genes, indicating that this stretch of DNA is duplicated in ...

9

### Demonstrating the robustness of population surveillance data: implications of error rates on demographic and mortality estimates

**random**error in the sense that it is assumed that any errors in measuring a variable are inde- pendent of the value of other ...terms,

**missing**10% or 20% of deaths, for example, can be considered a ...

9

### Dealing with missing data

8

### Predictive models for integrated pest management of the leaf beetle Chrysophtharta bimaculata in Eucalyptus nitens plantations in Tasmania

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