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Abstract of Emerging Trends in Scientific Research, 2014, Vol.1
DOI: 10.18488/journal.1002/2014.1/1002.1
1st
International Conference on Emerging Trends in
Scientific Research
15-16 March, 2014
Pearl International Hotel Kuala Lumpur, Malaysia
Conference Website: www.pakrdw.com
44
Paper ID: 193/14/1
st
ICETSR
Nonparametric Bootstrap Inference in a Multivariate Spatial-
Temporal Model
Abubakar Sali Asaad1
1
Department of Epidemiology and Biostatistics, College of Public Health, University of the Philippines
Manila, 625 Pedro Gil St., Ermita, Manila, philippines, 1000
Abstract
Nonparametric bootstrap inference in a multivariate spatial-temporal model is proposed to
verify two important assumptions namely, constant multivariate characteristics across
spatial locations and constant multivariate characteristics across time points. The
bootstrap normal confidence intervals and type-2 p-value for the multivariate
characteristics across spatial locations/points were constructed for the test procedures.
Results of the simulation studies indicate that the proposed test procedures are powerful
and is correctly size. The test procedures for multivariate characteristics across spatial
locations/time points are also robust for a wide range of data structures.

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Nonparametric Bootstrap Inference in a Multivariate Spatial- Temporal Model

  • 1. Abstract of Emerging Trends in Scientific Research, 2014, Vol.1 DOI: 10.18488/journal.1002/2014.1/1002.1 1st International Conference on Emerging Trends in Scientific Research 15-16 March, 2014 Pearl International Hotel Kuala Lumpur, Malaysia Conference Website: www.pakrdw.com 44 Paper ID: 193/14/1 st ICETSR Nonparametric Bootstrap Inference in a Multivariate Spatial- Temporal Model Abubakar Sali Asaad1 1 Department of Epidemiology and Biostatistics, College of Public Health, University of the Philippines Manila, 625 Pedro Gil St., Ermita, Manila, philippines, 1000 Abstract Nonparametric bootstrap inference in a multivariate spatial-temporal model is proposed to verify two important assumptions namely, constant multivariate characteristics across spatial locations and constant multivariate characteristics across time points. The bootstrap normal confidence intervals and type-2 p-value for the multivariate characteristics across spatial locations/points were constructed for the test procedures. Results of the simulation studies indicate that the proposed test procedures are powerful and is correctly size. The test procedures for multivariate characteristics across spatial locations/time points are also robust for a wide range of data structures.