856,035 research outputs found
A Software Tool for the Exponential Power Distribution: The normalp Package
In this paper we present the normalp package, a package for the statistical environment R that has a set of tools for dealing with the exponential power distribution. In this package there are functions to compute the density function, the distribution function and the quantiles from an exponential power distribution and to generate pseudo-random numbers from the same distribution. Moreover, methods concerning the estimation of the distribution parameters are described and implemented. It is also possible to estimate linear regression models when we assume the random errors distributed according to an exponential power distribution. A set of functions is designed to perform simulation studies to see the suitability of the estimators used. Some examples of use of this package are provided.
A combined tree growing technique for block-test scheduling under power constraints
A tree growing technique is used here together with classical scheduling algorithms in order to improve the test concurrency having assigned power dissipation limits. First of all, the problem of unequal-length block-test scheduling under power dissipation constraints is modeled as a tree growing problem. Then a combination of list and force-directed scheduling algorithms is adapted to tackle it. The goal of this approach is to achieve rapidly a test scheduling solution with a near-optimal test application time. This is initially achieved with the list approach. Then the power dissipation distribution of this solution is balanced by using a force-directed global priority function. The force-directed priority function is a distribution-graph based global priority function. A constant additive model is employed for power dissipation analysis and estimation. Based on test scheduling examples, the efficiency of this approach is discussed as compared to the other approaches
Transverse momentum distribution with radial flow in relativistic diffusion model
Large transverse momentum distributions of identified particles observed at
RHIC are analyzed by a relativistic stochastic model in the three dimensional
(non-Euclidean) rapidity space. A distribution function obtained from the model
is Gaussian-like in radial rapidity. It can well describe observed transverse
momentum distributions. Estimation of radial flow is made from the
analysis of distributions for in Au + Au Collisions.
Temperatures are estimated from observed large distributions under the
assumption that the distribution function approaches to the Maxwell-Boltzmann
distribution in the lower momentum limit. Power-law behavior of large
distribution is also derived from the model.Comment: 7 pages, 5 figures and 6 table
Exponentiated Extended Weibull-Power Series Class of Distributions
In this paper, we introduce a new class of distributions by compounding the
exponentiated extended Weibull family and power series family. This
distribution contains several lifetime models such as the complementary
extended Weibull-power series, generalized exponential-power series,
generalized linear failure rate-power series, exponentiated Weibull-power
series, generalized modified Weibull-power series, generalized Gompertz-power
series and exponentiated extended Weibull distributions as special cases. We
obtain several properties of this new class of distributions such as Shannon
entropy, mean residual life, hazard rate function, quantiles and moments. The
maximum likelihood estimation procedure via a EM-algorithm is presented.Comment: Accepted for publication Ciencia e Natura Journa
The Kumaraswamy Generalized Power Weibull Distribution
A new family of distributions called Kumaraswamy-generalized power Weibull (Kgpw) distribution is proposed and studied. This family has a number of well known sub-models such as Weibull, exponentiated Weibull, Kumaraswamy Weibull, generalized power Weibull and new sub-models, namely, exponentiated generalized power Weibull, Kumaraswamy generalized power exponential distributions. Some statistical properties of the new distribution include its moments, moment generating function, quantile function and hazard function are derived. In addition, maximum likelihood estimates of the model parameters are obtained. An application as well as comparisons of the Kgpw and its sub-distributions is given. Keywords: Generalized power Weibull distribution, Kumaraswamy distribution, Maximum likelihood estimation, Moment generating function, Hazard rate function.
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