‎Multivariate normal-Poisson model has been recently introduced as a special case of normal stable Tweedie models‎. ‎The model is composed of a univariate Poisson variable‎, ‎and the remaining variables given the Poisson one are independent Gaussian variables with variance the value of the Poisson component‎. ‎Two characterizations of this model are shown‎, ‎...

In this paper, a multivariate fundamental skew probit (MFSP) model is used to model correlated ordinal responses which are constructed from the multivariate fundamental skew normal (MFSN) distribution originate to the greater flexibility of MFSN. To achieve an appropriate VC structure for reaching reliable statistical inferences, many types of variance covariance (VC) structures are considered ...

In certain circumstances inference based on the likelihood function can be hindered by, for example, computational complexity; new applications of directional statistics to bioinformatics problems give many obvious examples. In such cases it is necessary to seek an alternative method of estimation. Two pseudolikelihoods, each based on conditional distributions, are assessed in terms of their ef...

introduction: 18f-choline pet-ct is an increasingly used technique in patients with prostate cancer. the main indication is to localise the disease in patients with biochemical recurrence. to accurately interpret 18f-choline pet, knowledge of normal tracer distribution is paramount. the aim of this study was to describe the normal distribution pattern of 18f-choline by measuring the maximum sta...

Sampling from a truncated multivariate normal distribution subject to multiple linear inequality constraints is a recurring problem in many areas in statistics and econometrics, such as the order restricted regressions, censored data models, and shape-restricted nonparametric regressions. However, the sampling problem still appears non-trivial due to the existence of the analytically intractabl...

The multivariate normal distribution is often the assumed distribution underlying data samples and it is widely used in pattern recognition and classiication 2]]3]]6]]7]. It is undoubtedly of great beneet to be able to generate random values and vectors from the distribution of choice given its suucient statistics or chosen parameters. We present a detailed account of the theory and algorithms ...

Multivariate control chats are generally used in situations where the simultaneous monitoring or control of two or more related quality characteristics is necessary. In most processes in the real world, distribution of the process characteristics are unknown or at least non-normal, so the non-parametric or distribution-free charts are desirable. Most non-parametric statistical process-control t...

Recently, commentators have suggested that the distributional form of cost data should be explicitly modelled to gain efficiency in estimating the population mean. We perform a series of simulation experiments to evaluate the usual sample mean and the mean estimator of a lognormal distribution, in the context of both theoretical distributions and three large empirical datasets. The sample mean ...

An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribut...