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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

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Decision making --- 519.226 --- 519.226 Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Inference and decision theory. Likelihood. Bayesian theory. Fiducial probability --- Congresses --- Congresses. --- Decision making - Congresses

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Dieses Open-Access-Buch gibt eine anwendungsorientierte Einführung in die logistische Regression. Ausgehend von Grundkenntnissen der linearen Regression wird diese zuerst als zweistufiges Modell interpretiert, was den Übergang zur logistischen Regression vereinfacht. Neben einer kompakten Einführung der entsprechenden Theorie liegt der Fokus auch auf der Umsetzung mit der Statistiksoftware R und der richtigen Formulierung der entsprechenden Ergebnisse. Alle Schritte werden anhand zahlreicher Beispiele illustriert. Hinzu kommt eine Einführung in die Klassifikation mit den entsprechenden Begriffen.

Probability & statistics --- Logistische Regression in R --- Logit-Modell --- Regressionsanalyse --- Zweistufiges Modell --- Binäre Variablen --- Log-Odds --- Wahrscheinlichkeit --- Maximum-Likelihood --- Klassifikation

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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.

Humanities --- Social interaction --- 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

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Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata, but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command to Stata. The book begins with an introduction to the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. Individual chapters then describe in detail each of the four types of likelihood evaluator programs and provide numerous examples, such as logit and probit regression, Weibull regression, random-effects linear regression, and the Cox proportional hazards model. Later chapters and appendixes provide additional details about the ml command, provide checklists to follow when writing evaluators, and show how to write your own estimation commands.

Quantitative methods in social research --- Social sciences --- Maximum likelihood estimation --- Stata --- Statistical methods --- Computer programs --- Maximum-Likelihood-Schätzung. --- Stata. --- Statistische Analyse. --- Computer programs. --- -519.5 --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- -Computer programs --- Stata (logiciel) --- Sciences sociales --- Méthodes statistiques --- Logiciels --- Logiciels. --- Social sciences - Statistical methods - Computer programs --- Méthodes statistiques