A Comparison of Size and Power of Tests of Hypotheses on Parameters Based on Two Generalized Lindley Distributions

Macaulay Okwuokenye; Karl E. Peace

doi:10.5351/CSAM.2015.22.3.233

A Comparison of Size and Power of Tests of Hypotheses on Parameters Based on Two Generalized Lindley Distributions

Macaulay Okwuokenye, Karl E. Peace

Biostatistics, Epidemiology, & Environmental Health Sciences

Biogen, Inc.

Research output: Contribution to journal › Article › peer-review

Abstract

This study compares two generalized Lindley distributions and assesses consistency between theoretical and analytical results. Data (complete and censored) assumed to follow the Lindley distribution are generated and analyzed using two generalized Lindley distributions, and maximum likelihood estimates of parameters from the generalized distributions are obtained. Size and power of tests of hypotheses on the parameters are assessed drawing on asymptotic properties of the maximum likelihood estimates. Results suggest that whereas size of some of the tests of hypotheses based on the considered generalized distributions are essentially α-level, some are possibly not; power of tests of hypotheses on the Lindley distribution parameter from the two distributions differs.

Original language	American English
Journal	Communications for Statistical Application and Methods
Volume	22
DOIs	https://doi.org/10.5351/CSAM.2015.22.3.233
State	Published - May 31 2015

Keywords

Death density function
Generalized Lindley distribution
Lindley distribution
Observed and censored times
Size and power of tests of hypotheses on time-to-event parameters
Survival function

DC Disciplines

Biostatistics
Community Health
Public Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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https://doi.org/10.5351/CSAM.2015.22.3.233

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title = "A Comparison of Size and Power of Tests of Hypotheses on Parameters Based on Two Generalized Lindley Distributions",

abstract = " This study compares two generalized Lindley distributions and assesses consistency between theoretical and analytical results. Data (complete and censored) assumed to follow the Lindley distribution are generated and analyzed using two generalized Lindley distributions, and maximum likelihood estimates of parameters from the generalized distributions are obtained. Size and power of tests of hypotheses on the parameters are assessed drawing on asymptotic properties of the maximum likelihood estimates. Results suggest that whereas size of some of the tests of hypotheses based on the considered generalized distributions are essentially α-level, some are possibly not; power of tests of hypotheses on the Lindley distribution parameter from the two distributions differs.",

keywords = "Death density function, Generalized Lindley distribution, Lindley distribution, Observed and censored times, Size and power of tests of hypotheses on time-to-event parameters, Survival function",

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year = "2015",

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AU - Okwuokenye, Macaulay

AU - Peace, Karl E.

PY - 2015/5/31

Y1 - 2015/5/31

N2 - This study compares two generalized Lindley distributions and assesses consistency between theoretical and analytical results. Data (complete and censored) assumed to follow the Lindley distribution are generated and analyzed using two generalized Lindley distributions, and maximum likelihood estimates of parameters from the generalized distributions are obtained. Size and power of tests of hypotheses on the parameters are assessed drawing on asymptotic properties of the maximum likelihood estimates. Results suggest that whereas size of some of the tests of hypotheses based on the considered generalized distributions are essentially α-level, some are possibly not; power of tests of hypotheses on the Lindley distribution parameter from the two distributions differs.

AB - This study compares two generalized Lindley distributions and assesses consistency between theoretical and analytical results. Data (complete and censored) assumed to follow the Lindley distribution are generated and analyzed using two generalized Lindley distributions, and maximum likelihood estimates of parameters from the generalized distributions are obtained. Size and power of tests of hypotheses on the parameters are assessed drawing on asymptotic properties of the maximum likelihood estimates. Results suggest that whereas size of some of the tests of hypotheses based on the considered generalized distributions are essentially α-level, some are possibly not; power of tests of hypotheses on the Lindley distribution parameter from the two distributions differs.

KW - Death density function

KW - Generalized Lindley distribution

KW - Lindley distribution

KW - Observed and censored times

KW - Size and power of tests of hypotheses on time-to-event parameters

KW - Survival function

UR - https://digitalcommons.georgiasouthern.edu/biostat-facpubs/112

UR - https://doi.org/10.5351/CSAM.2015.22.3.233

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DO - 10.5351/CSAM.2015.22.3.233

M3 - Article

VL - 22

JO - Communications for Statistical Application and Methods

JF - Communications for Statistical Application and Methods

ER -

A Comparison of Size and Power of Tests of Hypotheses on Parameters Based on Two Generalized Lindley Distributions

Abstract

Keywords

DC Disciplines

UN SDGs

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