A new generalization of the Lindley distribution is recently proposed by Ghitany et al. [1], called as the power Lindley distribution. Another generalization of the Lindley distribution was introduced by Nadarajah et al. [2], named as the generalized Lindley distribution. This paper proposes a more generalization of the Lindley distribution which generalizes the two. We refer to this new genera...

We introduce and evaluate via a Monte Carlo study a robust new estimation technique that fits distribution functions to grouped response time (RT) data, where the grouping is determined by sample quantiles. The new estimator, quantile maximum likelihood (QML), is more efficient and less biased than the best alternative estimation technique when fitting the commonly used ex-Gaussian distribution...

Using probability plots and Maximum Likelihood Estimation (MLE), we fit lognormal distributions to data compiled by Ershow et al. for daily intake of total water and tap water by three groups of women (controls, pregnant, and lactating; all between 15-49 years of age) in the United States. We also develop bivariate lognormal distributions for the joint distribution of water ingestion and body w...

he linear exponential distribution is a very well-known distribution for modeling lifetime data in reliability and medical studies.We introduce in this paper a new four-parameter generalized version of the transmuted generalized linear exponential distribution.We provide a comprehensive account of the mathematical properties of the new distributions. In particular, A closed-form expressions for...

We construct a copula from the multivariate skew t-distribution of Azzalini and Capitanio (2003). This copula can capture asymmetric and extreme dependence between variables, and it is one of the few that is effective when the number of dimensions is high. However, two problems arise when estimating the parameters by maximum likelihood estimation. Here, we solve these problems and provide a con...

The MemToolbox is a collection of MATLAB functions for modeling visual working memory. In support of its goal to provide a full suite of data analysis tools, the toolbox includes implementations of popular models of visual working memory, real and simulated data sets, Bayesian and maximum likelihood estimation procedures for fitting models to data, visualizations of data and fit, validation rou...

The limitations inherent in group versus case studies appear to lie in a complementary distribution, underscoring the importance of combining both strategies within a single research program. However, this compromise approach requires analytic tools that permit us to combine and evaluate individual and group data in a common format. Maximum likelihood estimation (MLE) belongs to a family of pro...

Structural equation modeling is a well-known technique for studying relationships among multivariate data. In practice, high dimensional nonnormal data with small to medium sample sizes are very common, and large sample theory, on which almost all modeling statistics are based, cannot be invoked for model evaluation with test statistics. The most natural method for nonnormal data, the asymptoti...

Effective denoising is vital for proper analysis and accurate quantitative measurements from magnetic resonance (MR) images. Even though many methods were proposed to denoise MR images, only few deal with the estimation of true signal from MR images acquired with phased-array coils. If the magnitude data from phased array coils are reconstructed as the root sum of squares, in the absence of noi...

The consistency and asymptotic normality of minimum contrast estimation (which includes the maximum likelihood estimation as a special case) is established if the sample is from a renewal process and the observation time tends to innnity. It is shown, that the conditions for consistency and asymptotic normality for maximum likelihood estimation are fulllled if the distribution of the time betwe...