ScholarGate
Assistente

Comparar métodos

Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.

Escore Z de Altman: Previsão de Falência Corporativa×Regressão Logística×
ÁreaFinançasEstatística para pesquisa
FamíliaRegression modelProcess / pipeline
Ano de origem19681958
Autor originalEdward AltmanDavid Roxbee Cox
TipoMultiple discriminant analysis scoring modelMethod
Fonte seminalAltman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. DOI ↗Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗
Outros nomesAltman's Z-Score Model, Multiple Discriminant Analysis Bankruptcy Model, Z-Score Financial Distress Model, Altman Z-Skorulogit model, binomial logistic regression, LR
Relacionados33
ResumoThe Altman Z-Score is a linear discriminant model developed by Edward I. Altman in 1968 to predict corporate bankruptcy using five accounting-based financial ratios. Derived through multiple discriminant analysis on a matched sample of 66 US manufacturing firms, the model combines liquidity, profitability, leverage, solvency, and activity ratios into a single composite score that classifies firms as financially sound, distressed, or in a grey zone.Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.
ScholarGateConjunto de dados
  1. v1
  2. 1 Fontes
  3. PUBLISHED
  1. v1
  2. 2 Fontes
  3. PUBLISHED

Ir para a pesquisa Baixar slides

ScholarGateComparar métodos: Altman Z-Score · Logistic Regression. Recuperado em 2026-06-20 de https://scholargate.app/pt/compare