Score test for missing at random or not

Wang, Hairu; Lu, Zhiping; Liu, Yukun

doi:10.48550/arxiv.2105.12921

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…where logit(z) := log {z/(1 − z)} for all 0 < z < 1 and h(x; α) is injective with respect to α and is known up to a finite-dimensional parameter α. This logistic model has been used in many previous studies, for example, Kim and Yu (2011); Shao and Wang (2016) and Wang et al (2021).…”

Section: Basic Setupmentioning

confidence: 99%

Imputation with verifiable identification condition for nonignorable missing outcomes

Beppu¹,

Morikawa²,

Im³

2022

Preprint

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Missing data often results in undesirable bias and loss of efficiency. These results become substantial problems when the response mechanism is nonignorable, such that the response model depends on the unobserved variable. It is often necessary to estimate the joint distribution of the unobserved variables and response indicators to further manage nonignorable nonresponse. However, model misspecification and identification issues prevent robust estimates, despite carefully estimating the target joint distribution. In this study we model the distribution of the observed parts and derived sufficient conditions for model identifiability, assuming a logistic distribution of the response mechanism and a generalized linear model as the main outcome model of interest. More importantly, the derived sufficient conditions are testable with the observed data and do not require any instrumental variables, which have often been assumed to guarantee model identifiability but cannot be practically determined beforehand. To analyse missing data, we propose a new fractional imputation method which incorporates verifiable identifiability using only the observed data. Furthermore, we present the performance of the proposed estimators in numerical studies and apply the proposed method to two sets of real data, namely, Opinion Poll for the 2022 South Korean Presidential Election, and public data collected from the US National Supported Work Evaluation Study.

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Section: Basic Setupmentioning

confidence: 99%

Imputation with verifiable identification condition for nonignorable missing outcomes

Beppu¹,

Morikawa²,

Im³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The latter type of nonresponse is of grave concern to survey statisticians. Because the missing data depends on both the observed and unobserved data and thus termed as nonignorable nonresponse [3,4]. Deleting such missingness will lead to biased estimates and wrong conclusion on the population parameters due to the introduction of complexities such as selection bias, undercoverage, reduced sample size, increased variability, imputation challenge, nonresponse bias, resource and cost implication [5].…”

Section: Introductionmentioning

confidence: 99%