Description Usage Arguments Details References Examples
Density, distribution function, quantile function and random generation for the reversal power logistic distribution with parameters mu, sigma and lambda.
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x, q |
vector of quantiles. |
lambda |
shape parameter. |
mu, sigma |
location and scale parameters. |
log, log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are P[X ≤ x ], otherwise, P[X > x]. |
p |
vector of probabilities. |
n |
number of observations. |
The reversal power Logistic distribution has density
f(x)=[λ/σ][exp(-(x-μ)/σ)/(1+exp(-(x-μ)/σ))^2][exp(-(x-μ)/σ)/(1+exp(-(x-μ)/σ)]^(λ-1), where -∞<μ<∞ is the location paramether, σ^2>0 the scale parameter and λ>0 the shape parameter.
Anyosa, S. A. C. (2017) Binary regression using power and reversal power links. Master's thesis in Portuguese. Interinstitutional Graduate Program in Statistics. Universidade de São Paulo - Universidade Federal de São Carlos. Available in https://repositorio.ufscar.br/handle/ufscar/9016.
Bazán, J. L., Torres -Avilés, F., Suzuki, A. K. and Louzada, F. (2017) Power and reversal power links for binary regressions: An application for motor insurance policyholders. Applied Stochastic Models in Business and Industry, 33(1), 22-34.
Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, volume 1, chapter 16. Wiley, New York.
Nagler J. (1994) Scobit: an alternative estimator to logit and probit. American Journal Political Science, 38(1), 230-255.
Prentice, R. L. (1976) A Generalization of the probit and logit methods for dose-response curves. Biometrika, 32, 761-768.
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