1,249 research outputs found
Kumaraswamy autoregressive moving average models for double bounded environmental data
In this paper we introduce the Kumaraswamy autoregressive moving average
models (KARMA), which is a dynamic class of models for time series taking
values in the double bounded interval following the Kumaraswamy
distribution. The Kumaraswamy family of distribution is widely applied in many
areas, especially hydrology and related fields. Classical examples are time
series representing rates and proportions observed over time. In the proposed
KARMA model, the median is modeled by a dynamic structure containing
autoregressive and moving average terms, time-varying regressors, unknown
parameters and a link function. We introduce the new class of models and
discuss conditional maximum likelihood estimation, hypothesis testing
inference, diagnostic analysis and forecasting. In particular, we provide
closed-form expressions for the conditional score vector and conditional Fisher
information matrix. An application to environmental real data is presented and
discussed.Comment: 25 pages, 4 tables, 4 figure
Some Extended Classes of Distributions: Characterizations and Properties
Based on a simple relationship between two truncated moments and certain functions of the th order statistic, we characterize some extended classes of distributions recently proposed in the statistical literature, videlicet Beta-G, Gamma-G, Kumaraswamy-G and McDonald-G. Several properties of these extended classes and some special cases are discussed. We compare these classes in terms of goodness-of-fit criteria using some baseline distributions by means of two real data sets
Another Generalized Transmuted Family of Distributions: Properties and Applications
We introduce and study general mathematical properties of a new generator of continuous distributions with two extra parameters called the Another generalized transmuted family of distributions. We present some special models. We investigate the asymptotes and shapes. The new density function can be expressed as a linear combination of exponentiated densities based on the same baseline distribution. We obtain explicit expressions for the ordinary and incomplete moments and generating functions, Bonferroni and Lorenz curves, asymptotic distribution of the extreme values, Shannon and Renyi entropies and order statistics, which hold for any baseline model, certain characterisations are presented. Further, we introduce a bivariate extensions of the new family. We discuss the dierent method of estimation of the model parameters and illustrate the potentiality of the family by means of two applications to real data. A brief simulation for evaluating Maximum likelihood estimator is done
Characterizations and Infinite Divisibility of Certain Recently Introduced Distributions IV
Certain characterizations of recently proposed univariate continuous distributions are presented in different directions. This work contains a good number of reintroduced distributions and may serve as a source of preventing the reinvention and/or duplication of the existing distributions in the future
- …