Linear Probability, Logit, and Probit Models

Aldrich, John H.; Nelson, Forrest D.

doi:10.4135/9781412984744

Cited by 2,001 publications

(911 citation statements)

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“…For the model predicting endogeneity, we used probit regression (Aldrich & Nelson, 1984). We also explored whether endogeneity affects citations; we surmised that given the collective knowledge of the academic market (as expressed in the citations an article receives), articles that have endogeneity problems might be less cited than those that do not.…”

Section: Estimation Strategymentioning

confidence: 99%

What makes articles highly cited?

Antonakis

Bastardoz

Liu

et al. 2014

The Leadership Quarterly

109

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We examined drivers of article citations using 776 articles that were published from 1990-2012 in a broad-based and high-impact social sciences journal, The Leadership Quarterly. These articles had 1,191 unique authors having published and received in total (at the time of their most recent article published in our dataset) 16,817 articles and 284,777 citations, respectively. Our models explained 66.6% of the variance in citations and showed that quantitative, review, method, and theory articles were significantly more cited than were qualitative articles or agentbased simulations. As concerns quantitative articles, which constituted the majority of the sample, our model explained 80.3% of the variance in citations; some methods (e.g., use of SEM) and designs (e.g., meta-analysis), as well as theoretical approaches (e.g., use of transformational, charismatic, or visionary type-leadership theories) predicted higher article citations. Regarding the statistical conclusion validity of quantitative articles, articles having endogeneity threats received significantly fewer citations than did those using a more robust design or an estimation procedure that ensured correct causal estimation. We make several general recommendations on how to improve research practice and article citations.

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Section: Estimation Strategymentioning

confidence: 99%

What makes articles highly cited?

Antonakis

Bastardoz

Liu

et al. 2014

The Leadership Quarterly

109

View full text Add to dashboard Cite

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“…follows a χ 2 distribution for a number of degrees of freedom m when the null hypothesis is true (Aldrich & Nelson 1984). The probability that a sum of m normally distributed random variables with mean 0 and variance 1 is larger than a value c LL is:…”

Section: Real Planetsmentioning

confidence: 99%

Interpreting the yield of transit surveys: are there groups in the known transiting planets population?

Fressin¹,

Guillot²,

Nesta³

2009

A&A

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Context. Each transiting planet discovered is characterized by 7 measurable quantities, that may or may not be linked. This includes those relative to the planet (mass, radius, orbital period, and equilibrium temperature) and those relative to the star (mass, radius, effective temperature, and metallicity). Correlations between planet mass and period, surface gravity and period, planet radius and star temperature have been previously observed among the 31 known transiting giant planets. Two classes of planets have been previously identified based on their Safronov number. Aims. We use the CoRoTlux transit surveys to compare simulated events to the sample of discovered planets and test the statistical significance of these correlations. Using a model proved to be able to match the yield of OGLE transit survey, we generate a large sample of simulated detections, in which we can statistically test the different trends observed in the small sample of known transiting planets. Methods. We first generate a stellar field with planetary companions based on radial velocity discoveries, use a planetary evolution model assuming a variable fraction of heavy elements to compute the characteristics of transit events, then apply a detection criterion that includes both statistical and red noise sources. We compare the yield of our simulated survey with the ensemble of 31 wellcharacterized giant transiting planets, using different statistical tools, including a multivariate logistic analysis to assess whether the simulated distribution matches the known transiting planets. Results. Our results satisfactorily match the distribution of known transiting planet characteristics. Our multivariate analysis shows that our simulated sample and observations are consistent to 76%. The mass vs. period correlation for giant planets first observed with radial velocity holds with transiting planets. The correlation between surface gravity and period can be explained as the combined effect of the mass vs. period lower limit and by the decreasing transit probability and detection efficiency for longer periods and higher surface gravity. Our model also naturally explains other trends, like the correlation between planetary radius and stellar effective temperature. Finally, we are also able to reproduce the previously observed apparent bimodal distribution of planetary Safronov numbers in 10% of our simulated cases, although our model predicts a continuous distribution. This shows that the evidence for the existence of two groups of planets with different intrinsic properties is not statistically significant.

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“…Over the last 35 years, the topic of company failure prediction has developed to a major research domain in corporate finance [3]- [5]. Academic researchers from all over the world have been developing a gigantic number of corporate failure prediction models, based on various types of modeling techniques [6]. Besides the classic crosssectional statistical methods, which have produced numerous failure prediction models, researchers have also been using several alternative methods for analyzing and predicting business failure [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Z Score Estimation for Indian Banking Sector

Pradhan¹

2014

IJTEF

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Abstract-From traditional times the Z score values have been constantly used for prediction of Bankruptcy. This has been vital to both the lenders and investors whose returns are based on solvency estimates. The terms of credit have gone a U turn from the traditional times to the modern scenario today. The basic concern of prediction is to evaluate the terms of credit and ensure repayment safely. Z score has been used as a tool to evaluate the credibility of the firms. This paper provides the Z score value for the public sector banks. This value is useful when these banks demand loans from the RBI or any other funding agency. The usage of back propagation neural network is to forecast the internal parameters of Z score and then use these internal parameters to forecast the Z score value up to 2020. Thus the paper emphasizes the usage of BPNN for prediction of bankruptcy for public sector banks in India.  Index Terms-Bankruptcy, internal parameters of Z score Prediction, Z score value.

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Linear Probability, Logit, and Probit Models

Cited by 2,001 publications

References 0 publications

What makes articles highly cited?

What makes articles highly cited?

Interpreting the yield of transit surveys: are there groups in the known transiting planets population?

Z Score Estimation for Indian Banking Sector

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