Use of auxiliary data in semi-parametric spatial regression with nonignorable missing responses

2006 ◽  
Vol 6 (4) ◽  
pp. 321-336 ◽  
Author(s):  
Marco Geraci ◽  
Matteo Bottai
Keyword(s):  
Spatial Regression ◽  
Auxiliary Data ◽  
Missing Responses ◽  
Nonignorable Missing

scholarly journals A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses

2017 ◽  
Vol 112 (518) ◽  
pp. 484-496 ◽  
Author(s):  
Giampiero Marra ◽  
Rosalba Radice ◽  
Till Bärnighausen ◽  
Simon N. Wood ◽  
Mark E. McGovern
Keyword(s):  
Simultaneous Equation ◽  
Hiv Prevalence ◽  
Equation Approach ◽  
Missing Responses ◽  
Nonignorable Missing

Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses

Metrika ◽  
2019 ◽  
Vol 83 (5) ◽  
pp. 545-568
Author(s):  
Xianwen Ding ◽  
Jiandong Chen ◽  
Xueping Chen
Keyword(s):  
Quantile Regression ◽  
Missing Responses ◽  
Nonignorable Missing

Modeling Nonignorable Missing Data in Speeded Tests

2008 ◽  
Vol 68 (6) ◽  
pp. 907-922 ◽  
Author(s):  
Cees A. W. Glas ◽  
Jonald L. Pimentel
Keyword(s):  
Missing Data ◽  
Marginal Likelihood ◽  
Distribution Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Missing Responses ◽  
Item Parameters ◽  
Maximum Marginal Likelihood ◽  
Nonignorable Missing ◽  
Linear Restrictions

In tests with time limits, items at the end are often not reached. Usually, the pattern of missing responses depends on the ability level of the respondents; therefore, missing data are not ignorable in statistical inference. This study models data using a combination of two item response theory (IRT) models: one for the observed response data and one for the missing data indicator. The missing data indicator is modeled using a sequential model with linear restrictions on the item parameters. The models are connected by the assumption that the respondents' latent proficiency parameters have a joint multivariate normal distribution. Model parameters are estimated by maximum marginal likelihood. Simulations show that treating missing data as ignorable can lead to considerable bias in parameter estimates. Including an IRT model for the missing data indicator removes this bias. The method is illustrated with data from an intelligence test with a time limit.

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