1,124 research outputs found
Multiple imputation and selection of ordinal level 2 predictors in multilevel models. An analysis of the relationship between student ratings and teacher beliefs and practices
The paper is motivated by the analysis of the relationship between ratings
and teacher practices and beliefs, which are measured via a set of binary and
ordinal items collected by a specific survey with nearly half missing
respondents. The analysis, which is based on a two-level random effect model,
must face two about the items measuring teacher practices and beliefs: (i)
these items level 2 predictors severely affected by missingness; (ii) there is
redundancy in the number of items and the number of categories of their
measurement scale. tackle the first issue by considering a multiple imputation
strategy based on information at both level 1 and level 2. For the second
issue, we consider regularization techniques for ordinal predictors, also
accounting for the multilevel data structure. The proposed solution combines
existing methods in an original way to solve specific problem at hand, but it
is generally applicable to settings requiring to select predictors affected by
missing values. The results obtained with the final model out that some teacher
practices and beliefs are significantly related to ratings about teacher
ability to motivate students.Comment: Presented at the 12th International Multilevel Conference is held
April 9-10, 2019 , Utrech
Estimating conditional density of missing values using deep Gaussian mixture model
We consider the problem of estimating the conditional probability
distribution of missing values given the observed ones. We propose an approach,
which combines the flexibility of deep neural networks with the simplicity of
Gaussian mixture models (GMMs). Given an incomplete data point, our neural
network returns the parameters of Gaussian distribution (in the form of Factor
Analyzers model) representing the corresponding conditional density. We
experimentally verify that our model provides better log-likelihood than
conditional GMM trained in a typical way. Moreover, imputation obtained by
replacing missing values using the mean vector of our model looks visually
plausible.Comment: A preliminary version of this paper appeared as an extended abstract
at the ICML 2020 Workshop on The Art of Learning with Missing Value
Fractional Imputation in Survey Sampling: A Comparative Review
Fractional imputation (FI) is a relatively new method of imputation for
handling item nonresponse in survey sampling. In FI, several imputed values
with their fractional weights are created for each missing item. Each
fractional weight represents the conditional probability of the imputed value
given the observed data, and the parameters in the conditional probabilities
are often computed by an iterative method such as EM algorithm. The underlying
model for FI can be fully parametric, semiparametric, or nonparametric,
depending on plausibility of assumptions and the data structure.
In this paper, we give an overview of FI, introduce key ideas and methods to
readers who are new to the FI literature, and highlight some new development.
We also provide guidance on practical implementation of FI and valid
inferential tools after imputation. We demonstrate the empirical performance of
FI with respect to multiple imputation using a pseudo finite population
generated from a sample in Monthly Retail Trade Survey in US Census Bureau.Comment: 26 pages, 2 figure
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