Some results on classifier selection with missing covariates

Mojirsheibani, Majid

doi:10.1007/s00184-010-0340-6

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The terms p(Z, i) and q(X, i) are often referred to as the missingness probability mechanisms. For more recent results on classification with missing covariates, one may also refer to Mojirsheibani (2012) and Mojirsheibani and Chenouri (2011).…”

Section: Classification With Missing Covariatesmentioning

confidence: 99%

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Classification on convex sets in the presence of missing covariates

Demirdjian,

Mojirsheibani

2018

Preprint

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A number of results related to statistical classification on convex sets are presented. In particular, the focus is on the case where some of the covariates in the data and observation being classified can be missing. The form of the optimal classifier is derived when the class-conditional densities are uniform over convex regions. In practice, the underlying convex sets are often unknown and must be estimated with a set of data. In this case, the convex hull of a set of points is shown to be a consistent estimator of the underlying convex set. The problem of estimation is further complicated since the number of points in each convex hull is itself a random variable. The corresponding plug-in version of the optimal classifier is derived and shown to be Bayes consistent.

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Section: Classification With Missing Covariatesmentioning

confidence: 99%

“…In fact, Mojirsheibani and Montazeri (2007) derived the optimal classifier in the presence of missing covariates, which is in general different from (1.1). For more recent results on classification with missing covariates, one may also refer to Mojirsheibani (2012) and Mojirsheibani and Chenouri (2011).…”

Section: Introductionmentioning

confidence: 99%

Classification on convex sets in the presence of missing covariates

Demirdjian,

Mojirsheibani

2018

Preprint

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On histogram-based regression and classification with incomplete data

Eric

Mojirsheibani

2020

Metrika

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We consider the problem of nonparametric regression with possibly incomplete covariate vectors. The proposed estimators, which are based on histogram methods, are fully nonparametric and straightforward to implement. The presence of incomplete covariates is handled by an inverse weighting method, where the weights are estimates of the conditional probabilities of having incomplete covariate vectors. We also derive various exponential bounds on the L 1 norms of our estimators, which can be used to establish strong consistency results for the corresponding, closely related, problem of nonparametric classification with missing covariates. As the main focus and application of our results, we consider the problem of pattern recognition and statistical classification in the presence of incomplete covariates and propose histogram classifiers that are asymptotically optimal.

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On the $$L_p$$ norms of kernel regression estimators for incomplete data with applications to classification

Reese

Mojirsheibani

2016

Stat Methods Appl

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Some results on classifier selection with missing covariates

Cited by 3 publications

References 9 publications

Classification on convex sets in the presence of missing covariates

Classification on convex sets in the presence of missing covariates

On histogram-based regression and classification with incomplete data

On the $$L_p$$ norms of kernel regression estimators for incomplete data with applications to classification

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