A jackknife maximum likelihood estimator for the probit model

Sapra, Sunil K.

doi:10.1080/13504850110050683

Cited by 7 publications

(9 citation statements)

References 3 publications

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“…There are certainly other methods available for adjusting the OOR estimator. For example, Sapra (2002) demonstrated how the jackknife procedure (Efron, 1982) can be applied to estimate regression coefficients for the Probit model; the same general approach could be used to derive a bias-corrected OOR estimate. Future work might identify a bias estimator with better properties than our proposed estimator.…”

Section: Discussionmentioning

confidence: 99%

An Additional Measure of Overall Effect Size for Logistic Regression Models

Allen¹,

2008

Journal of Educational and Behavioral Statistics

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Users of logistic regression models often need to describe the overall predictive strength, or effect size, of the model’s predictors. Analogs of R2 have been developed, but none of these measures are interpretable on the same scale as effects of individual predictors. Furthermore, R2 analogs are not invariant to the base rate (overall proportion of successes), making it difficult to compare effect sizes across data sets. The authors propose a measure of overall effect size that is interpretable on the same scale as effects of individual predictors and is invariant to the base rate. They explore the properties of the overall odds ratio and illustrate its use through an example. They also provide interpretive guidance and illustrate how statistical software can be used to compute the proposed measure.

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Section: Discussionmentioning

confidence: 99%

An Additional Measure of Overall Effect Size for Logistic Regression Models

Allen¹,

2008

Journal of Educational and Behavioral Statistics

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“…Jackknife liner regression analysis was used to determine the effects of nutrition and PA on eighth grade reading, math, and science achievement scores. The jackknife, a sample reuse statistical methodology, is used to enlarge the sample data and re‐estimate the model in order to generate parameter estimates that approximate the true population parameter . Thus, the goal of the jackknife is to estimate a parameter of a population of interest from a random sample of data from this population.…”

Section: Methodsmentioning

confidence: 99%

“…For example, Jackknife resampling uses the provided full data set, or a given data generating mechanism (eg, coin toss) that is a model of the process of interest to produce a new sample of simulated data, and examines the results of those samples . Also, the jackknife resampling method reduces sample bias and is an interval estimator . Given that data from the ECLSK‐8 data set was used from the final wave of data collection for this study, the use of jackknife as a sample reuse methodology for the eighth grade sample allows for an approximation of the true population parameters.…”

Section: Methodsmentioning

confidence: 99%

The Link Between Nutrition and Physical Activity in Increasing Academic Achievement

Asigbee

Whitney

Peterson

2018

Journal of School Health

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“…The standard error of the corrected estimate can be calculated based on the following equation (Sapra, 2002):…”

Section: Estimating Standard Error Of the Corrected Oormentioning

confidence: 99%

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression

Marcus

2012

Educational and Psychological Measurement

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This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model strength. The authors also illustrate and test a jackknife procedure to correct for the bias in the OOR and estimate its standard error. An example applying the OOR to evaluate logistic regression models predicting organizational turnover is provided. The authors discuss implications and offer recommendations for using the OOR to quantify and compare the effectiveness of logistic regression models in applied research.Keywords effect size, logistic regression, odds ratio, R square Many important criterion variables in educational and organizational research are dichotomous in nature (e.g., college dropout, turnover), and thus require multiple logistic regression (MLOGR) analysis. Unfortunately, users of the method often find themselves disadvantaged by lack of a meaningful index to describe the overall strength or

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A jackknife maximum likelihood estimator for the probit model

Cited by 7 publications

References 3 publications

An Additional Measure of Overall Effect Size for Logistic Regression Models

An Additional Measure of Overall Effect Size for Logistic Regression Models

The Link Between Nutrition and Physical Activity in Increasing Academic Achievement

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression

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