Robin Mitra - Publications & Outputs - Research Portal

Dr Robin Mitra

Formerly at Lancaster University

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2008
Published
Estimating risks of identification disclosure in partially synthetic data
Reiter, J. P. & Mitra, R., 1/10/2008.
Research output: Working paper
2009
Published
Two level stochastic search variable selection in GLMs with missing predictors
Mitra, R. & Dunson, D. D., 1/02/2009.
Research output: Working paper
Published
Estimating risks of identification disclosure in partially synthetic data
Reiter, J. P. & Mitra, R., 1/04/2009, In: Journal of Privacy and Confidentiality. 1, 1, p. 99-110 12 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Published
Estimating propensity scores with missing covariate data using general location mixture models
Mitra, R. & Reiter, J. P., 1/08/2009.
Research output: Working paper
2010
Published
On the equivalence of posterior inference based on retrospective and prospective likelihoods
Ghosh, M., Song, J., Forster, J. J., Mitra, R. & Mukherjee, B., 2010.
Research output: Working paper
Published
Two-level stochastic search variable selection in GLMs with missing predictors
Mitra, R. & Dunson, D., 1/01/2010, In: International Journal of Biostatistics. 6, 1
Research output: Contribution to Journal/Magazine › Journal article › peer-review
2011
Published
Estimating propensity scores with missing covariate data using general location mixture models
Mitra, R. & Reiter, J. P., 15/03/2011, In: Statistics in Medicine. 30, 6, p. 627-641 15 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Published
A comparison of two methods of estimating propensity scores after multiple imputation
Mitra, R. & Reiter, J. P., 1/04/2011.
Research output: Working paper
Published
Propensity score matching with missing covariates via iterated, sequential multiple imputation
Mitra, R. & Reiter, J. P., 1/04/2011.
Research output: Working paper
2012
Published
On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case-control study of colorectal cancer
Ghosh, M., Song, J., Forster, J. J., Mitra, R. & Mukherjee, B., 10/09/2012, In: Statistics in Medicine. 31, 20, p. 2196-2208 13 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
2013
Published
Are independent parameter draws necessary for multiple imputation?
Hu, J., Mitra, R. & Reiter, J. P., 2013, In: American Statistician. 67, 3, p. 143-149 7 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
2015
Published
Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets
Rashid, S., Mitra, R. & Steele, R. J., 1/12/2015, In: Computational Statistics and Data Analysis. 92, p. 84-96 13 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
2016
Published
A comparison of two methods of estimating propensity scores after multiple imputation
Mitra, R. & Reiter, J. P., 1/02/2016, In: Statistical Methods in Medical Research. 25, 1, p. 188-204 17 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Published
Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models
Lee, M. & Mitra, R., 1/03/2016, In: Computational Statistics and Data Analysis. 95, p. 24-38 15 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
2017
Published
Optimal design for experiments with possibly incomplete observations
Lee, K. M., Biedermann, S. & Mitra, R., 2017, In: Statistica Sinica. 26 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Published
On the Use of Cauchy Prior Distributions for Bayesian Logistic Regression
Ghosh, J., Li, Y. & Mitra, R., 7/03/2017, In: Bayesian Analysis. 13, 2, p. 359-383 25 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
2018
Published
Optimal design when outcome values are not missing at random
Lee, K., Mitra, R. & Biedermann, S., 1/10/2018, In: Statistica Sinica. 28, 4, p. 1821-1838 18 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
2019
Published
D-optimal designs for multiarm trials with dropouts
Lee, K. M., Biedermann, S. & Mitra, R., 1/07/2019, In: Statistics in Medicine. 38, 15, p. 2749-2766 18 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
2020
Published
Confidentiality challenges in releasing longitudinally linked data
Mitra, R., Blanchard, S., Dove, I., Tudor, C. & Spicer, K., 2020, In: Transactions on Data Privacy. 13, 2, p. 151-170 20 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Published
Multiply imputing missing values arising by design in transplant survival data
Mitra, R., Kimber, A., Pankhurst, L. & Collett, D., 1/09/2020, In: Biometrical Journal. 62, 5, p. 1192-1207 16 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
2023
Published
Learning from data with structured missingness
Mitra, R., McGough, S. F., Chakraborti, T., Holmes, C., Copping, R., Hagenbuch, N., Biedermann, S., Noonan, J., Lehmann, B., Shenvi, A., Doan, X. V., Leslie, D., Bianconi, G., Sanchez-Garcia, R., Davies, A., Mackintosh, M., Andrinopoulou, E-R., Basiri, A., Harbron, C. & MacArthur, B. D., 25/01/2023, In: Nature Machine Intelligence. 5, 1, p. 13-23 11 p.
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Published
A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching
Mitra, R., 31/03/2023, In: Biometrical Journal. 65, 3, 17 p., 2100284.
Research output: Contribution to Journal/Magazine › Journal article › peer-review

Research

Dr Robin Mitra

Estimating risks of identification disclosure in partially synthetic data

Two level stochastic search variable selection in GLMs with missing predictors

Estimating risks of identification disclosure in partially synthetic data

Estimating propensity scores with missing covariate data using general location mixture models

On the equivalence of posterior inference based on retrospective and prospective likelihoods

Two-level stochastic search variable selection in GLMs with missing predictors

Estimating propensity scores with missing covariate data using general location mixture models

A comparison of two methods of estimating propensity scores after multiple imputation

Propensity score matching with missing covariates via iterated, sequential multiple imputation

On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case-control study of colorectal cancer

Are independent parameter draws necessary for multiple imputation?

Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets

A comparison of two methods of estimating propensity scores after multiple imputation

Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models

Optimal design for experiments with possibly incomplete observations

On the Use of Cauchy Prior Distributions for Bayesian Logistic Regression

Optimal design when outcome values are not missing at random

D-optimal designs for multiarm trials with dropouts

Confidentiality challenges in releasing longitudinally linked data

Multiply imputing missing values arising by design in transplant survival data

Learning from data with structured missingness

A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching

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