Author
Mário de Castro
Other affiliations: University of São Paulo, Universidade Federal do Espírito Santo
Bio: Mário de Castro is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Errors-in-variables models & Estimator. The author has an hindex of 17, co-authored 83 publications receiving 1882 citations. Previous affiliations of Mário de Castro include University of São Paulo & Universidade Federal do Espírito Santo.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a new family of generalized distributions for double-bounded random processes with hydrological applications is described, including Kw-normal, Kw-Weibull and Kw-Gamma distributions.
Abstract: Kumaraswamy [Generalized probability density-function for double-bounded random-processes, J. Hydrol. 462 (1980), pp. 79–88] introduced a distribution for double-bounded random processes with hydrological applications. For the first time, based on this distribution, we describe a new family of generalized distributions (denoted with the prefix ‘Kw’) to extend the normal, Weibull, gamma, Gumbel, inverse Gaussian distributions, among several well-known distributions. Some special distributions in the new family such as the Kw-normal, Kw-Weibull, Kw-gamma, Kw-Gumbel and Kw-inverse Gaussian distribution are discussed. We express the ordinary moments of any Kw generalized distribution as linear functions of probability weighted moments (PWMs) of the parent distribution. We also obtain the ordinary moments of order statistics as functions of PWMs of the baseline distribution. We use the method of maximum likelihood to fit the distributions in the new class and illustrate the potentiality of the new model with a...
742 citations
TL;DR: Methods to identify plants by analysing leaf complexity based on estimating their fractal dimension, using a computational program to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification.
238 citations
TL;DR: In this paper, Chen et al. proposed a new Bayesian model for survival data with a surviving fraction, which is the unification of the long-term survival models proposed by Berkson and Gage.
140 citations
TL;DR: In this paper, the authors developed a flexible cure rate survival model by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell Poisson distribution, which includes as special cases some of the well-known cure rate models discussed in the literature.
128 citations
TL;DR: A flexible cure rate survival model by assuming the number of competing causes of the event of interest to follow a compound weighted Poisson distribution is developed, which gives an interesting and realistic interpretation of the biological mechanism of the occurrence of event ofinterest.
Abstract: In this paper, we develop a flexible cure rate survival model by assuming the number of competing causes of the event of interest to follow a compound weighted Poisson distribution. This model is more flexible in terms of dispersion than the promotion time cure model. Moreover, it gives an interesting and realistic interpretation of the biological mechanism of the occurrence of event of interest as it includes a destructive process of the initial risk factors in a competitive scenario. In other words, what is recorded is only from the undamaged portion of the original number of risk factors.
65 citations
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1,677 citations
Journal Article•
1,240 citations
744 citations
10 Apr 2013
TL;DR: In this article, a new method is proposed for generating families of continuous distributions, where a random variable is used to transform another random variable and the resulting family, the $$T$$¯¯ -=-=-=-=-=-=-=-=-=-=-=-=- family of distributions, has a connection with the hazard functions and each generated distribution is considered as a weighted hazard function.
Abstract: In this paper, a new method is proposed for generating families of continuous distributions. A random variable $$X$$
, “the transformer”, is used to transform another random variable $$T$$
, “the transformed”. The resulting family, the $$T$$
-
$$X$$
family of distributions, has a connection with the hazard functions and each generated distribution is considered as a weighted hazard function of the random variable $$X$$
. Many new distributions, which are members of the family, are presented. Several known continuous distributions are found to be special cases of the new distributions.
694 citations
559 citations