Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

We discuss theory and application of extended object tracking. This task is challenging as sensor noise prevents a correct association of the measurements to their sources on the object, the shape itself might be unknown a priori, and due to occlusion effects, only parts of the object are visible at a given time. We propose an approach to track the parameters of arbitrary objects, which provides new solutions to the above challenges, and marks a significant advance to the state of the art.

Tracking Bayesschätzer Microsoft Kinect Formschätzung Partial LikelihoodTracking Bayesian Estimation Microsoft Kinect Shape Fitting Partial Likelihood

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

519.226 --- Decision making --- -Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Mathematical models --- Mathematical models. --- -Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- 519.226 Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- -519.226 Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Deciding

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In recent years, the advances and abilities of computer software have substantially increased the number of scientific publications that seek to introduce new probabilistic modelling frameworks, including continuous and discrete approaches, and univariate and multivariate models. Many of these theoretical and applied statistical works are related to distributions that try to break the symmetry of the normal distribution and other similar symmetric models, mainly using Azzalini's scheme. This strategy uses a symmetric distribution as a baseline case, then an extra parameter is added to the parent model to control the skewness of the new family of probability distributions. The most widespread and popular model is the one based on the normal distribution that produces the skewed normal distribution. In this Special Issue on symmetric and asymmetric distributions, works related to this topic are presented, as well as theoretical and applied proposals that have connections with and implications for this topic. Immediate applications of this line of work include different scenarios such as economics, environmental sciences, biometrics, engineering, health, etc. This Special Issue comprises nine works that follow this methodology derived using a simple process while retaining the rigor that the subject deserves. Readers of this Issue will surely find future lines of work that will enable them to achieve fruitful research results.

positive and negative skewness --- ordering --- fitting distributions --- Epsilon-skew-Normal --- Epsilon-skew-Cauchy --- bivariate densities --- generalized Cauchy distributions --- asymmetric bimodal distribution --- bimodal --- maximum likelihood --- slashed half-normal distribution --- kurtosis --- likelihood --- EM algorithm --- flexible skew-normal distribution --- skew Birnbaum–Saunders distribution --- bimodality --- maximum likelihood estimation --- Fisher information matrix --- maximum likelihood estimates --- type I and II censoring --- skewness coefficient --- Weibull censored data --- truncation --- half-normal distribution --- probabilistic distribution class --- normal distribution --- identifiability --- moments --- power-normal distribution

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Bayesian statistical decision theory. --- Statistique bayésienne --- 519.226 --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- 519.226 Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Bayesian statistical decision theory --- Statistique bayésienne

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Research without statistics is like water in the sand; the latter is necessary to reap the benefits of the former. This collection of articles is designed to bring together different approaches to applied statistics. The studies presented in this book are a tiny piece of what applied statistics means and how statistical methods find their usefulness in different fields of research from theoretical frames to practical applications such as genetics, computational chemistry, and experimental design. This book presents several applications of the statistics: A new continuous distribution with five parameters—the modified beta Gompertz distribution; A method to calculate the p-value associated with the Anderson–Darling statistic; An approach of repeated measurement designs; A validated model to predict statement mutations score; A new family of structural descriptors, called the extending characteristic polynomial (EChP) family, used to express the link between the structure of a compound and its properties. This collection brings together authors from Europe and Asia with a specific contribution to the knowledge in regards to theoretical and applied statistics.

molecular descriptors --- compound symmetry --- Anderson–Darling test (AD) --- software testing --- probability --- characteristic polynomial (ChP) --- mutation testing --- C20 fullerene --- fullerene congeners --- machine learning --- maximum likelihood estimation --- gompertz distribution --- modified beta generator --- structure–property relationships --- repeated measurement designs --- Monte Carlo simulation