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Minimax robust active learning for approximately specified regression models

Canadian journal of statistics, 2018-03, Vol.46 (1), p.104-122

2018

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Robust discrimination designs

Journal of the Royal Statistical Society. Series B, Statistical methodology, 2009-09, Vol.71 (4), p.805-829

Wiens, Douglas P.

2009

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Robustness of design in dose-response studies

Journal of the Royal Statistical Society. Series B, Statistical methodology, 2011-03, Vol.73 (2), p.215-238

2011

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Robust designs for series estimation

Computational statistics & data analysis, 2008-05, Vol.52 (9), p.4305-4324

2008

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Robust estimators and designs for field experiments

Journal of statistical planning and inference, 2008, Vol.138 (1), p.93-104

2008

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A comparative study of robust designs for M-estimated regression models

Computational statistics & data analysis, 2010-06, Vol.54 (6), p.1683-1695

2010

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Robust prediction and extrapolation designs for misspecified generalized linear regression models

Journal of statistical planning and inference, 2008, Vol.138 (1), p.30-46

2008

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Robust designs for one-point extrapolation

Journal of statistical planning and inference, 2008-05, Vol.138 (5), p.1339-1357

2008

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New criteria for robust integer-valued designs in linear models

Computational statistics & data analysis, 2006-11, Vol.51 (2), p.723-736

2006

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Minimax Robust Designs and Weights for Approximately Specified Regression Models with Heteroscedastic Errors

Journal of the American Statistical Association, 1998-12, Vol.93 (444), p.1440-1450

Wiens, Douglas P.

1998

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Locally D-optimal designs for multistage models and heteroscedastic polynomial regression models

Journal of statistical planning and inference, 2006-11, Vol.136 (11), p.4059-4070

2006

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Robust allocation schemes for clinical trials with prognostic factors

Journal of statistical planning and inference, 2005, Vol.127 (1), p.323-340

Wiens, Douglas P.

2005

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Robust regression designs for approximate polynomial models

Journal of statistical planning and inference, 2003-12, Vol.117 (2), p.305-321

2003

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Robustness of design for the testing of lack of fit and for estimation in binary response models : Variable selection and robust procedures

Computational statistics & data analysis, 2010, Vol.54 (12), p.3371-3378

WIENS, Douglas P

2010

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Robust Designs Based on the Infinitesimal Approach

Journal of the American Statistical Association, 1997-12, Vol.92 (440), p.1503-1511

1997

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Robust designs for misspecified logistic models: Special Issue on Metaheuristics, Combinatorial Optimization and Design of Experiments

Journal of statistical planning and inference, 2009, Vol.139 (1), p.3-15

2009

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Integer-Valued, Minimax Robust Designs for Estimation and Extrapolation in Heteroscedastic, Approximately Linear Models

Journal of the American Statistical Association, 2000-09, Vol.95 (451), p.807-818

2000

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Robust weights and designs for biased regression models: Least squares and generalized M-estimation

Journal of statistical planning and inference, 2000-02, Vol.83 (2), p.395-412

Wiens, Douglas P.

2000

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Minimax designs for approximately linear regression

Journal of statistical planning and inference, 1992-06, Vol.31 (3), p.353-371

Wiens, Douglas P.

1992

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Minimax regression designs for approximately linear models with autocorrelated errors

Journal of statistical planning and inference, 1996-10, Vol.55 (1), p.95-106

1996

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Minimax robust active learning for approximately specified regression models

Robust discrimination designs

Robustness of design in dose-response studies

Robust designs for series estimation

Robust estimators and designs for field experiments

A comparative study of robust designs for M-estimated regression models

Robust prediction and extrapolation designs for misspecified generalized linear regression models

Robust designs for one-point extrapolation

New criteria for robust integer-valued designs in linear models

Minimax Robust Designs and Weights for Approximately Specified Regression Models with Heteroscedastic Errors

Locally D-optimal designs for multistage models and heteroscedastic polynomial regression models

Robust allocation schemes for clinical trials with prognostic factors

Robust regression designs for approximate polynomial models

Robustness of design for the testing of lack of fit and for estimation in binary response models : Variable selection and robust procedures

Robust Designs Based on the Infinitesimal Approach

Robust designs for misspecified logistic models: Special Issue on Metaheuristics, Combinatorial Optimization and Design of Experiments

Integer-Valued, Minimax Robust Designs for Estimation and Extrapolation in Heteroscedastic, Approximately Linear Models

Robust weights and designs for biased regression models: Least squares and generalized M-estimation

Minimax designs for approximately linear regression

Minimax regression designs for approximately linear models with autocorrelated errors

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