On The Gamma-Half Normal Distribution and Its Applications

A new distribution, the gamma-half normal distribution, is proposed and studied. Various structural properties of the gamma-half normal distribution are derived. The shape of the distribution may be unimodal or bimodal. Results for moments, limit behavior, mean deviations and Shannon entropy are provided. To estimate the model parameters, the method of maximum likelihood estimation is proposed. Three real-life data sets are used to illustrate the applicability of the gamma-half normal distribution

On the Gamma-Logistic Distribution

A new generalization of the logistic distribution is defined and studied, namely, the gamma-logistic distribution. Various properties of the gamma-logistic are obtained. The structural analysis of the distribution includes moments, mode, quantiles, skewness, kurtosis, Shannon\u27s entropy and order statistics. The method of maximum likelihood estimation is proposed for estimating the model parameters. For illustrative purposes, a real data set is analyzed as an application of the gamma-logistic distribution

A new generalization of the normal distribution: the gamma-normal distribution and its applications

Statistical distributions are commonly applied to describe real world phenomena. Due to the usefulness of statistical distributions, their theory is widely studied and new distributions are developed. The interest in developing more flexible statistical distributions remains strong in statistics profession. Many generalized classes of distributions have been developed and applied to describe various phenomena. Recently, Alzaatreh et al. (2014) developed a new method to generate family of distributions and called it the gamma-X family of distributions

A new generalization of the normal distribution: the gamma-normal distribution and its applications

Statistical distributions are commonly applied to describe real world phenomena. Due to the usefulness of statistical distributions, their theory is widely studied and new distributions are developed. The interest in developing more flexible statistical distributions remains strong in statistics profession. Many generalized classes of distributions have been developed and applied to describe various phenomena. Recently, Alzaatreh et al. (2014) developed a new method to generate family of distributions and called it the gamma-X family of distributions

Gamma-Pareto Distribution and Its Applications

A new distribution, the gamma-Pareto, is defined and studied and various properties of the distribution are obtained. Results for moments, limiting behavior and entropies are provided. The method of maximum likelihood is proposed for estimating the parameters and the distribution is applied to fit three real data sets

Disaggregating high-resolution gas metering data using pattern recognition

© 2018 Elsevier B.V. Growing concern about the scale and extent of the gap between predicted and actual energy performance of new and retrofitted UK homes has led to a surge in the development of new tools and technologies trying to address the problem. A vital aspect of this work is to improve ease and accuracy of measuring in-use performance to better understand the extent of the gap and diagnose its causes. Existing approaches range from low cost but basic assessments allowing very limited diagnosis, to intensively instrumented experiments that provide detail but are expensive and highly disruptive, typically requiring the installation of specialist monitoring equipment and often vacating the house for several days. A key challenge in reducing the cost and difficulty of complex methods in occupied houses is to disaggregate space heating energy from that used for other uses without installing specialist monitoring equipment. This paper presents a low cost, non-invasive approach for doing so for a typical occupied UK home where space heating, hot water and cooking are provided by gas. The method, using dynamic pattern matching of total gas consumption measurements, typical of those provided by a smart meter, was tested by applying it to two occupied houses in the UK. The findings revealed that this method was successful in detecting heating patterns in the data and filtering out coinciding use

How to Measure Evidence: Bayes Factors or Relative Belief Ratios?

Both the Bayes factor and the relative belief ratio satisfy the principle of evidence and so can be seen to be valid measures of statistical evidence. The question then is: which of these measures of evidence is more appropriate? Certainly Bayes factors are commonly used. It is argued here that there are questions concerning the validity of a current commonly used definition of the Bayes factor and, when all is considered, the relative belief ratio is a much more appropriate measure of evidence. Several general criticisms of these measures of evidence are also discussed and addressed

A Supervised Feature Selection Approach Based on Global Sensitivity

In this paper we propose a wrapper method for feature selection in supervised learning. It is based on the global sensitivity analysis; a variancebased technique that determines the contribution of each feature and their interactions to the overall variance of the target variable. First-order and total Sobol sensitivity indices are used for feature ranking. Feature selection based on global sensitivity is a wrapper method that utilizes the trained model to evaluate feature importance. It is characterized by its computational efficiency because both sensitivity indices are calculated using the same Monte Carlo integral. A publicly available data set in machine learning is used to demonstrate the application of the algorithm

A New Weibull–Pareto Distribution: Properties and Applications

Many distributions have been used as lifetime models. In this article, we propose a new three-parameter Weibull–Pareto distribution, which can produce the most important hazard rate shapes, namely, constant, increasing, decreasing, bathtub, and upsidedown bathtub. Various structural properties of the new distribution are derived including explicit expressions for the moments and incomplete moments, Bonferroni and Lorenz curves, mean deviations, mean residual life, mean waiting time, and generating and quantile functions. The Rényi and q entropies are also derived. We obtain the density function of the order statistics and their moments. The model parameters are estimated by maximum likelihood and the observed information matrix is determined. The usefulness of the new model is illustrated by means of two real datasets on Wheaton river flood and bladder cancer. In the two applications, the new model provides better fits than the Kumaraswamy–Pareto, beta-exponentiated Pareto, beta-Pareto, exponentiated Pareto, and Pareto models