Spherical Logistic Distribution

Moghimbeygi, Meisam; Golalizadeh, Mousa

doi:10.1007/s40304-018-00171-2

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, numerous other RS families have been proposed in the literature: Section 2.3.2 of Ley and Verdebout (2017a) summarises many of them. The spherical logistic distribution of Moghimbeygi and Golalizadeh (2020) provides a multimodal and RS extension of the vMF, with a closedform normalising constant when d = 2. Another recent addition is the highly tractable spherical Cauchy distribution of Kato and McCullagh (2020), which extends the WC to S d and has a very simple normalising constant.…”

Section: Models For Spherical Datamentioning

confidence: 99%

Recent advances in directional statistics

Pewsey

García‐Portugués

2021

TEST

View full text Add to dashboard Cite

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere, and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper, we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (Wiley 1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, space situational awareness, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. AnWe are most grateful to three anonymous referees and, in alphabetical order, to

show abstract

Section: Models For Spherical Datamentioning

confidence: 99%

Recent advances in directional statistics

Pewsey

García‐Portugués

2021

TEST

View full text Add to dashboard Cite

show abstract

“…The estimation of parameters, identifiability, and choosing the number of mixing components and parameters are among the well-known challenges in the application of mixture distributions. Furthermore, when the empirical density of the data is highly asymmetric, it can result in a misleading statistical inference of the parameters [42]. Multimodal distributions, which represent the random behaviour of data with multi-mode presence, can provide better model fitting.…”

Section: Protein Structure Applicationmentioning

confidence: 99%

Möbius Transformation-Induced Distributions Provide Better Modelling for Protein Architecture

et al. 2021

View full text Add to dashboard Cite

Proteins are found in all living organisms and constitute a large group of macromolecules with many functions. Proteins achieve their operations by adopting distinct three-dimensional structures encoded within the sequence of the constituent amino acids in one or more polypeptides. New, more flexible distributions are proposed for the MCMC sampling method for predicting protein 3D structures by applying a Möbius transformation to the bivariate von Mises distribution. In addition to this, sine-skewed versions of the proposed models are introduced to meet the increasing demand for modelling asymmetric toroidal data. Interestingly, the marginals of the new models lead to new multimodal circular distributions. We analysed three big datasets consisting of bivariate information about protein domains to illustrate the efficiency and behaviour of the proposed models. These newly proposed models outperformed mixtures of well-known models for modelling toroidal data. A simulation study was carried out to find the best method for generating samples from the proposed models. Our results shed new light on proposal distributions in the MCMC sampling method for predicting the protein structure environment.

show abstract

New Directional Residuals to Treat Shape Changes Using Spherical Regression Models

Moghimbeygi

Golalizadeh

2020

Iran J Sci Technol Trans Sci

View full text Add to dashboard Cite

Spherical Logistic Distribution

Cited by 3 publications

References 10 publications

Recent advances in directional statistics

Recent advances in directional statistics

Möbius Transformation-Induced Distributions Provide Better Modelling for Protein Architecture

New Directional Residuals to Treat Shape Changes Using Spherical Regression Models

Contact Info

Product

Resources

About