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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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A vector autoregression model with time-varying coefficients is used to examine the evolution of wage cyclicality in four Latin American economies: Brazil, Chile, Colombia and Mexico, during the period 1980-2010. Wages are highly pro-cyclical in all countries up to the mid-1990s except in Chile. Wage cyclicality declines thereafter, especially in Brazil and Colombia. This decline in wage cyclicality is in accordance with declining real-wage flexibility in a low-inflation environment. Controlling for compositional effects caused by changes in labor force participation along the business cycle does not alter these results.

Bayesian Estimation --- Downward Wage Rigidity --- Economic Theory & Research --- Environment --- Environmental Economics & Policies --- Governance --- Indexation --- Labor Markets --- Labor Policies --- Macroeconomics and Economic Growth --- Real Wage Cyclicality --- Social Protections and Labor --- Time Varying Coefficients --- Vector Autoregression --- Youth & Governance

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Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data, models, parameters and parameter restriction uncertainties, in a unified and coherent framework. This book contributes to this literature by collecting a set of carefully evaluated contributions that are grouped amongst two topics in financial economics. The first three papers refer to macro-finance issues for real economy, including the elasticity of factor substitution (ES) in the Cobb–Douglas production function, the effects of government public spending components, and quantitative easing, monetary policy and economics. The last three contributions focus on cryptocurrency and stock market predictability. All arguments are central ingredients in the current economic discussion and their importance has only been further emphasized by the COVID-19 crisis.

Technology: general issues --- unconventional monetary policy --- transmission channel --- Bayesian TVP-SV-VAR --- Bayesian econometrics --- portfolio choice --- sentiments --- stock market predictability --- cryptocurrency --- Bitcoin --- forecasting --- point forecast --- density forecast --- dynamic model averaging --- dynamic model selection --- forgetting factors --- military and civilian spending --- DSGE model --- fiscal policy --- monetary policy --- Bayesian estimation --- Bayesian VAR --- density forecasting --- time-varying volatility --- ES --- CES function --- Bayesian nonlinear mixed-effects regression --- MCMC methods --- macroeconomic and financial applications

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Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing.

Capital assets pricing model. --- Machine learning --- Finance --- Economic aspects. --- Mathematical models. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Capital asset pricing model --- CAPM (Capital assets pricing model) --- Pricing model, Capital assets --- Capital --- Investments --- Mathematical models --- Advances in Financial Learning. --- Bayesian estimation. --- Bayesian regression. --- Igor Halperin. --- Machine Learning in Finance. --- Marcos Lopez de Prado. --- Matthew Dixon. --- Paul Bilokon. --- Supervised learning. --- asset prices. --- cross-section of stock returns. --- data-driven methods of tuning. --- elastic-net estimator. --- factor models. --- firm fundamentals. --- high-dimensional prediction. --- market efficiency. --- mean-variance optimization framework. --- neural networks. --- out-of-sample performance. --- regularization. --- return predictability. --- ridge regression. --- risk premia estimation. --- trees and random forests. --- Machine learning. --- Financial applications.

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In the last two decades, the understanding of complex dynamical systems underwent important conceptual shifts. The catalyst was the infusion of new ideas from the theory of critical phenomena (scaling laws, renormalization group, etc.), (multi)fractals and trees, random matrix theory, network theory, and non-Shannonian information theory. The usual Boltzmann–Gibbs statistics were proven to be grossly inadequate in this context. While successful in describing stationary systems characterized by ergodicity or metric transitivity, Boltzmann–Gibbs statistics fail to reproduce the complex statistical behavior of many real-world systems in biology, astrophysics, geology, and the economic and social sciences.The aim of this Special Issue was to extend the state of the art by original contributions that could contribute to an ongoing discussion on the statistical foundations of entropy, with a particular emphasis on non-conventional entropies that go significantly beyond Boltzmann, Gibbs, and Shannon paradigms. The accepted contributions addressed various aspects including information theoretic, thermodynamic and quantum aspects of complex systems and found several important applications of generalized entropies in various systems.

Research & information: general --- Mathematics & science --- ecological inference --- generalized cross entropy --- distributional weighted regression --- matrix adjustment --- entropy --- critical phenomena --- renormalization --- multiscale thermodynamics --- GENERIC --- non-Newtonian calculus --- non-Diophantine arithmetic --- Kolmogorov–Nagumo averages --- escort probabilities --- generalized entropies --- maximum entropy principle --- MaxEnt distribution --- calibration invariance --- Lagrange multipliers --- generalized Bilal distribution --- adaptive Type-II progressive hybrid censoring scheme --- maximum likelihood estimation --- Bayesian estimation --- Lindley’s approximation --- confidence interval --- Markov chain Monte Carlo method --- Rényi entropy --- Tsallis entropy --- entropic uncertainty relations --- quantum metrology --- non-equilibrium thermodynamics --- variational entropy --- rényi entropy --- tsallis entropy --- landsberg—vedral entropy --- gaussian entropy --- sharma—mittal entropy --- α-mutual information --- α-channel capacity --- maximum entropy --- Bayesian inference --- updating probabilities --- n/a --- Kolmogorov-Nagumo averages --- Lindley's approximation --- Rényi entropy --- rényi entropy --- landsberg-vedral entropy --- sharma-mittal entropy