On Improving the Performance of Logistic Regression Analysis Via Extreme Ranking

Hani Samawi

doi:10.1007/978-3-030-42196-0_15

On Improving the Performance of Logistic Regression Analysis Via Extreme Ranking

Hani Samawi

Biostatistics, Epidemiology, & Environmental Health Sciences

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Abstract

<p> <p id="x-x-x-Par1"> Logistic regression models for dichotomous or ordinal dependent variables is one of the generalized linear models. They have been frequently applied in several fields. In this chapter, we present more efficient and powerful performance of the logistic regression models analysis when a modified extreme ranked set sampling (modified ERSS) or moving extreme ranked set sampling (MERSS) are used and further improving the performance when a modified Double extreme ranked set sampling (modified DERSS) is used. We propose that ranking could be performed based on an available and easy to rank auxiliary variable which is associated with the response variable. Analytically and through simulations, we showed the superiority performance of the logistics regression analysis when modified ERSS, MERSS, and DERSS are used compared with using the simple random sample (SRS). For illustration purposes of the procedures developed, we use a real dataset from 2011/12 National Survey of Children’s Health (NSCH). </p></p>

Original language	American English
Title of host publication	Computational and Methodological Statistics and Biostatistics
DOIs	https://doi.org/10.1007/978-3-030-42196-0_15
State	Published - Aug 11 2020

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Biostatistics
Environmental Public Health
Epidemiology
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abstract = " Logistic regression models for dichotomous or ordinal dependent variables is one of the generalized linear models. They have been frequently applied in several fields. In this chapter, we present more efficient and powerful performance of the logistic regression models analysis when a modified extreme ranked set sampling (modified ERSS) or moving extreme ranked set sampling (MERSS) are used and further improving the performance when a modified Double extreme ranked set sampling (modified DERSS) is used. We propose that ranking could be performed based on an available and easy to rank auxiliary variable which is associated with the response variable. Analytically and through simulations, we showed the superiority performance of the logistics regression analysis when modified ERSS, MERSS, and DERSS are used compared with using the simple random sample (SRS). For illustration purposes of the procedures developed, we use a real dataset from 2011/12 National Survey of Children’s Health (NSCH). ",

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AB - Logistic regression models for dichotomous or ordinal dependent variables is one of the generalized linear models. They have been frequently applied in several fields. In this chapter, we present more efficient and powerful performance of the logistic regression models analysis when a modified extreme ranked set sampling (modified ERSS) or moving extreme ranked set sampling (MERSS) are used and further improving the performance when a modified Double extreme ranked set sampling (modified DERSS) is used. We propose that ranking could be performed based on an available and easy to rank auxiliary variable which is associated with the response variable. Analytically and through simulations, we showed the superiority performance of the logistics regression analysis when modified ERSS, MERSS, and DERSS are used compared with using the simple random sample (SRS). For illustration purposes of the procedures developed, we use a real dataset from 2011/12 National Survey of Children’s Health (NSCH).

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UR - https://doi.org/10.1007/978-3-030-42196-0_15

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DO - 10.1007/978-3-030-42196-0_15

M3 - Chapter

BT - Computational and Methodological Statistics and Biostatistics

ER -

On Improving the Performance of Logistic Regression Analysis Via Extreme Ranking

Abstract

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