Probit or Logit? Which is the better model to predict the longevity of seeds?

2020 ◽  
Vol 30 (1) ◽  
pp. 49-58
Author(s):  
Rute Q. de Faria ◽  
Amanda R. P. dos Santos ◽  
Deoclecio J. Amorim ◽  
Renato F. Cantão ◽  
Edvaldo A. A. da Silva ◽  
...  
Keyword(s):  
Survival Data ◽  
Logit Model ◽  
Probit Model ◽  
Estimation Procedure ◽  
Logistic Distribution ◽  
Logit Models ◽  
The Central Limit Theorem ◽  
Probit Models ◽  
Logit Function ◽  
Logit And Probit Models

AbstractThe prediction of seed longevity (P50) is traditionally performed by the use of the Probit model. However, due to the fact that the survival data are of binary origin (0,1), the fit of the model can be compromised by the non-normality of the residues. Consequently, this leads to prediction losses, despite the data being partially smoothed by Probit and Logit models. A possibility to reduce the effect of non-normality of the data would be to apply the principles of the central limit theorem, which states that non-normal residues tend to be normal as the n sample is increased. The Logit and Probit models differ in their normal and logistic distribution. Therefore, we developed a new estimation procedure by using a small increase of the n sample and tested it in the Probit and Logit functions to improve the prediction of P50. The results showed that the calculation of P50 by increasing the n samples from 4 to 6 replicates improved the index of correctness of the prediction. The Logit model presented better performance when compared with the Probit model, indicating that the estimation of P50 is more adequate when the adjustment of the data is performed by the Logit function.

scholarly journals Heteroskedasticity in One-Way Error Component Probit Models

Econometrics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 35
Author(s):  
Richard Kouamé Moussa
Keyword(s):  
Monte Carlo ◽  
Robustness Analysis ◽  
Probit Model ◽  
Estimation Procedure ◽  
Error Component ◽  
Ratio Test ◽  
Probit Models ◽  
Power Of The Test ◽  
Individual Effects ◽  
High Degree

This paper introduces an estimation procedure for a random effects probit model in presence of heteroskedasticity and a likelihood ratio test for homoskedasticity. The cases where the heteroskedasticity is due to individual effects or idiosyncratic errors or both are analyzed. Monte Carlo simulations show that the test performs well in the case of high degree of heteroskedasticity. Furthermore, the power of the test increases with larger individual and time dimensions. The robustness analysis shows that applying the wrong approach may generate misleading results except for the case where both individual effects and idiosyncratic errors are modelled as heteroskedastic.

scholarly journals An Extension of Log-Logistic Distribution for Analyzing Survival Data

2020 ◽  
pp. 789-801
Author(s):  
Aliya Syed Malik ◽  
S.P. Ahmad
Keyword(s):  
Survival Data ◽  
Real Life ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Logistic Distribution ◽  
Data Sets ◽  
Alpha Power ◽  
Real Life Data ◽  
Proposed Model ◽  
New Distribution

In this paper, a new generalization of Log Logistic Distribution using Alpha Power Transformation is proposed. The new distribution is named Alpha Power Log-Logistic Distribution. A comprehensive account of some of its statistical properties are derived. The maximum likelihood estimation procedure is used to estimate the parameters. The importance and utility of the proposed model are proved empirically using two real life data sets.

scholarly journals Poverty, Inequality and Millennium Development Goals’ Expenditure: A Probabilistic Linkage

2015 ◽  
Vol 3 (11) ◽  
pp. 653-663
Author(s):  
Bello Malam Sa’idu
Keyword(s):  
Millennium Development Goals ◽  
Data Series ◽  
Logit Models ◽  
Single Digit ◽  
Probit Models ◽  
Probabilistic Linkage ◽  
Unit Increase ◽  
Development Goals ◽  
Logit And Probit Models ◽  
Economic Imbalances

The objective of this paper is to investigate the linkage between poverty, inequality and Millennium Development Goals’ (MDGs) expenditure. To achieve the set objective, probit and logit models were empirically employed using a panel data series. The results revealed that a unit increase in expenditure on MDGs would lead to increase in poverty by a single digit and income inequality by double digits. This is not to blame the MDG funding or discourage it. Plausibly the expenditure on MDGs has been constrained due to technical, managerial, institutional, macro-economic imbalances, and policy bottlenecks. Therefore, government and agencies should ameliorate these constraints. Consequently, this work has originated applied logit and probit models to explore poverty-inequality-MDGs’ expenditure nexus.

scholarly journals Modeling Credit Risk: An Application of the Rough Set Methodology

2013 ◽  
Author(s):  
Reyes Samaniego Medina ◽  
Maria Jose Vazquez Cueto
Keyword(s):  
Discriminant Analysis ◽  
Credit Risk ◽  
Rough Set ◽  
Financial Risk ◽  
Decision Rules ◽  
Probability Of Default ◽  
Logit Models ◽  
Basel Accords ◽  
Probit Models ◽  
Logit And Probit Models

The Basel Accords encourages credit entities to implement their own models for measuring financial risk. In this paper, we focus on the use of internal ratings-based (IRB) models for the assessment of credit risk and, specifically, on one component that models the probability of default (PD). The traditional methods used for modelling credit risk, such as discriminant analysis and logit and probit models, start with several statistical restrictions. The rough set methodology avoids these limitations and as such is an alternative to the classic statistical methods. We apply the rough set methodology to a database of 106 companies that are applicants for credit. We obtain ratios that can best discriminate between financially sound and bankrupt companies, along with a series of decision rules that will help detect operations that are potentially in default. Finally, we compare the results obtained using the rough set methodology to those obtained using classic discriminant analysis and logit models. We conclude that the rough set methodology presents better risk classification results.  

Research on SUE DTA Model with Constant Demand Based on Probit Model

2011 ◽  
Vol 374-377 ◽  
pp. 2605-2609
Author(s):  
Lei Shi ◽  
Li Gao
Keyword(s):  
Logit Model ◽  
Existence And Uniqueness ◽  
Probit Model ◽  
The Other ◽  
A Algorithm ◽  
Model Based

Logit model is among the most important model in SUE DTA study. A lot of work have been done based on Logit model. As the other very important SUE DTA model, Probit model has not been the focus of many researcher. This paper presents a SUE model based on Probit model, which aims at building up the Probit model with constant demand. The existence and uniqueness of the model is presented, Finally, a algorithm is given.

Cyber Risk Assessment and Mitigation (CRAM) Framework Using Logit and Probit Models for Cyber Insurance

2017 ◽  
Vol 21 (5) ◽  
pp. 997-1018 ◽  
Author(s):  
Arunabha Mukhopadhyay ◽  
Samir Chatterjee ◽  
Kallol K. Bagchi ◽  
Peteer J. Kirs ◽  
Girja K. Shukla
Keyword(s):  
Risk Assessment ◽  
Cyber Insurance ◽  
Probit Models ◽  
Logit And Probit Models ◽  
Cyber Risk

Evaluating Predictive power of Data Envelopment Analysis Technique Compared with Logit and Probit Models in Predicting Corporate Bankruptcy

2012 ◽  
Vol 02 (09) ◽  
pp. 38-46
Author(s):  
Khalili Araghi Maryam ◽  
Makvandi Sara
Keyword(s):  
Data Envelopment Analysis ◽  
Predictive Power ◽  
Environmental Changes ◽  
Stock Exchange ◽  
Rapid Development ◽  
Manufacturing Firms ◽  
Data Envelopment ◽  
Probit Models ◽  
Analysis Technique ◽  
Logit And Probit Models

Simultaneous with extensive environmental changes and the rapid development of technology which has increasingly accelerated economy, competitiveness economical enterprises have restricted earning profit and make probable closing of bankrupt firms. Thus it seems necessary to find a model that can predict financial crisis and bankruptcy of companies. Nowadays occurrence of significant progress in other sciences, such as computer and math attract the attention of the financial scholars toward designing and using more exact patterns like Data Envelopment Analysis (DEA). For this purpose, this study uses DEA technique to predict the bankruptcy likelihood of manufacturing firms and also compare its predictability with2 methods : Logit and Probit models. Study sample includes all manufacturing firms listed in Stock Exchange of Tehran from 2000-2010. The results showed that the accuracy of the designed model under DEA technique is %72 and the predictability of Logit and Probit models has been81, and %80 respectively. The results also showed DEA was proved to be an effective tool for predicting bankruptcy likelihood of manufacturing firms; but,it acted less efficient than Logit and Probit models.

A Comparison of Two Logit Models in the Analysis of Qualitative Marketing Data

1979 ◽  
Vol 16 (4) ◽  
pp. 533-538 ◽  
Author(s):  
David Flath ◽  
E. W. Leonard
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Logit Model ◽  
Weighted Least Squares ◽  
Interaction Effects ◽  
Logit Models ◽  
Marketing Data

The authors compare the application of two logit models for the analysis of qualitative marketing data. A weighted least squares logit model is compared with a maximum likelihood logit model different from that mentioned by Green et ai. Empirical applications are used to compare the models. Suggestions are presented for interpreting and reporting the results of logit-type models, with special attention to interaction effects.

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