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This book contributes to the empirical literature on economic and human development from five different perspectives: the first chapter provides a new statistical test for bimodality of densities with an application to income data. The second chapter analyzes the worlds cross-country distribution of income and challenges the so called Twin Peaks-claim. The third chapter focuses on the world income distribution and resulting implications for poverty reduction, pro-poor growth and the evolution of global inequality. The fourth chapter estimates the welfare effects of recently negotiated Economic Partnership Agreements between the EU and African countries. Finally, the fifth chapter investigates whether democracy leads to higher levels of health and education.

Economic development --- Commercial treaties. --- Economic forecasting. --- Economics --- Forecasting --- Economic indicators --- Econometric models. --- Trade agreements (Commerce) --- Competition, International --- Foreign trade regulation --- Treaties --- Reciprocity (Commerce) --- AA / International- internationaal --- LDC / Developping Countries - Pays En Développement --- AFR / Africa - Afrika - Afrique --- 313 --- 338.8 --- 334.153.2 --- 321.4 --- Levenswijze en levensstandaard. Levensminimum. sociale indicatoren (Studiën) --- Economische groei --- Ontwikkelingssamenwerking. Europees Ontwikkelingsfonds --- Democratie --- Contribution --- Cross-Country Income Distribution --- Demogracy and Human Development --- Development --- Economic --- Economic Partnership Agreements --- Empirics --- Empiricsi --- Human --- Likelihood-Ratio-Test for Bimodality --- Vollmer --- World Income Distribution

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• Applications of Information Theory to Epidemiology collects recent research findings on the analysis of diagnostic information and epidemic dynamics. • The collection includes an outstanding new review article by William Benish, providing both a historical overview and new insights. • In research articles, disease diagnosis and disease dynamics are viewed from both clinical medicine and plant pathology perspectives. Both theory and applications are discussed. • New theory is presented, particularly in the area of diagnostic decision-making taking account of predictive values, via developments of the predictive receiver operating characteristic curve. • New applications of information theory to the analysis of observational studies of disease dynamics in both human and plant populations are presented.

Research & information: general --- Biology, life sciences --- Ebola model --- Caputo derivative --- Caputo-Fabrizio derivative --- Atangana-Baleanu derivative --- numerical results --- entropy --- information theory --- multiple diagnostic tests --- mutual information --- relative entropy --- balance --- Jensen-Shannon divergence --- observational study --- selection bias --- probability --- forecast --- likelihood ratio --- positive predictive value --- negative predictive value --- diagnostic information --- Shannon entropy --- epidemic model --- transient behavior --- vaccination and treatment intervention controls --- diagnostic test --- evaluation --- ROC curve --- PROC curve --- binormal --- prevalence --- Bayes' rule --- leaf plot --- expected mutual information --- predictive ROC curve --- PV-ROC curve --- SS-ROC curve --- SS/PV-ROC plot --- empirical --- urinary bladder cancer --- sensitivity --- specificity --- HIV/AIDS epidemic --- regression model --- Newton-Raphson procedure --- Fisher scoring algorithm --- time series --- early detection --- Asiatic citrus canker --- latent class --- field diagnostic --- scent signature --- direct assay --- deployment --- average mutual information --- stochastic processes --- deterministic dynamics --- Ebola model --- Caputo derivative --- Caputo-Fabrizio derivative --- Atangana-Baleanu derivative --- numerical results --- entropy --- information theory --- multiple diagnostic tests --- mutual information --- relative entropy --- balance --- Jensen-Shannon divergence --- observational study --- selection bias --- probability --- forecast --- likelihood ratio --- positive predictive value --- negative predictive value --- diagnostic information --- Shannon entropy --- epidemic model --- transient behavior --- vaccination and treatment intervention controls --- diagnostic test --- evaluation --- ROC curve --- PROC curve --- binormal --- prevalence --- Bayes' rule --- leaf plot --- expected mutual information --- predictive ROC curve --- PV-ROC curve --- SS-ROC curve --- SS/PV-ROC plot --- empirical --- urinary bladder cancer --- sensitivity --- specificity --- HIV/AIDS epidemic --- regression model --- Newton-Raphson procedure --- Fisher scoring algorithm --- time series --- early detection --- Asiatic citrus canker --- latent class --- field diagnostic --- scent signature --- direct assay --- deployment --- average mutual information --- stochastic processes --- deterministic dynamics

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• Applications of Information Theory to Epidemiology collects recent research findings on the analysis of diagnostic information and epidemic dynamics. • The collection includes an outstanding new review article by William Benish, providing both a historical overview and new insights. • In research articles, disease diagnosis and disease dynamics are viewed from both clinical medicine and plant pathology perspectives. Both theory and applications are discussed. • New theory is presented, particularly in the area of diagnostic decision-making taking account of predictive values, via developments of the predictive receiver operating characteristic curve. • New applications of information theory to the analysis of observational studies of disease dynamics in both human and plant populations are presented.

Ebola model --- Caputo derivative --- Caputo–Fabrizio derivative --- Atangana–Baleanu derivative --- numerical results --- entropy --- information theory --- multiple diagnostic tests --- mutual information --- relative entropy --- balance --- Jensen–Shannon divergence --- observational study --- selection bias --- probability --- forecast --- likelihood ratio --- positive predictive value --- negative predictive value --- diagnostic information --- Shannon entropy --- epidemic model --- transient behavior --- vaccination and treatment intervention controls --- diagnostic test --- evaluation --- ROC curve --- PROC curve --- binormal --- prevalence --- Bayes’ rule --- leaf plot --- expected mutual information --- predictive ROC curve --- PV-ROC curve --- SS-ROC curve --- SS/PV-ROC plot --- empirical --- urinary bladder cancer --- sensitivity --- specificity --- HIV/AIDS epidemic --- regression model --- Newton–Raphson procedure --- Fisher scoring algorithm --- time series --- early detection --- Asiatic citrus canker --- latent class --- field diagnostic --- scent signature --- direct assay --- deployment --- average mutual information --- stochastic processes --- deterministic dynamics --- n/a --- Caputo-Fabrizio derivative --- Atangana-Baleanu derivative --- Jensen-Shannon divergence --- Bayes' rule --- Newton-Raphson procedure

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In geodesy and geoinformation science, as well as in many other technical disciplines, it is often not possible to directly determine the desired target quantities. Therefore, the unknown parameters must be linked with the measured values by a mathematical model which consists of the functional and the stochastic models. The functional model describes the geometrical–physical relationship between the measurements and the unknown parameters. This relationship is sufficiently well known for most applications. With regard to the stochastic model, two problem domains of fundamental importance arise: 1. How can stochastic models be set up as realistically as possible for the various geodetic observation methods and sensor systems? 2. How can the stochastic information be adequately considered in appropriate least squares adjustment models? Further questions include the interpretation of the stochastic properties of the computed target values with regard to precision and reliability and the use of the results for the detection of outliers in the input data (measurements). In this Special Issue, current research results on these general questions are presented in ten peer-reviewed articles. The basic findings can be applied to all technical scientific fields where measurements are used for the determination of parameters to describe geometric or physical phenomena.

History of engineering & technology --- EM-algorithm --- multi-GNSS --- PPP --- process noise --- observation covariance matrix --- extended Kalman filter --- machine learning --- GNSS phase bias --- sequential quasi-Monte Carlo --- variance reduction --- autoregressive processes --- ARMA-process --- colored noise --- continuous process --- covariance function --- stochastic modeling --- time series --- elementary error model --- terrestrial laser scanning --- variance-covariance matrix --- terrestrial laser scanner --- stochastic model --- B-spline approximation --- Hurst exponent --- fractional Gaussian noise --- generalized Hurst estimator --- very long baseline interferometry --- sensitivity --- internal reliability --- robustness --- CONT14 --- Errors-In-Variables Model --- Total Least-Squares --- prior information --- collocation vs. adjustment --- mean shift model --- variance inflation model --- outlierdetection --- likelihood ratio test --- Monte Carlo integration --- data snooping --- GUM analysis --- geodetic network adjustment --- stochastic properties --- random number generator --- Monte Carlo simulation --- 3D straight line fitting --- total least squares (TLS) --- weighted total least squares (WTLS) --- nonlinear least squares adjustment --- direct solution --- singular dispersion matrix --- laser scanning data --- EM-algorithm --- multi-GNSS --- PPP --- process noise --- observation covariance matrix --- extended Kalman filter --- machine learning --- GNSS phase bias --- sequential quasi-Monte Carlo --- variance reduction --- autoregressive processes --- ARMA-process --- colored noise --- continuous process --- covariance function --- stochastic modeling --- time series --- elementary error model --- terrestrial laser scanning --- variance-covariance matrix --- terrestrial laser scanner --- stochastic model --- B-spline approximation --- Hurst exponent --- fractional Gaussian noise --- generalized Hurst estimator --- very long baseline interferometry --- sensitivity --- internal reliability --- robustness --- CONT14 --- Errors-In-Variables Model --- Total Least-Squares --- prior information --- collocation vs. adjustment --- mean shift model --- variance inflation model --- outlierdetection --- likelihood ratio test --- Monte Carlo integration --- data snooping --- GUM analysis --- geodetic network adjustment --- stochastic properties --- random number generator --- Monte Carlo simulation --- 3D straight line fitting --- total least squares (TLS) --- weighted total least squares (WTLS) --- nonlinear least squares adjustment --- direct solution --- singular dispersion matrix --- laser scanning data

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This book develops a general analysis and synthesis framework for impulsive and hybrid dynamical systems. Such a framework is imperative for modern complex engineering systems that involve interacting continuous-time and discrete-time dynamics with multiple modes of operation that place stringent demands on controller design and require implementation of increasing complexity--whether advanced high-performance tactical fighter aircraft and space vehicles, variable-cycle gas turbine engines, or air and ground transportation systems. Impulsive and Hybrid Dynamical Systems goes beyond similar treatments by developing invariant set stability theorems, partial stability, Lagrange stability, boundedness, ultimate boundedness, dissipativity theory, vector dissipativity theory, energy-based hybrid control, optimal control, disturbance rejection control, and robust control for nonlinear impulsive and hybrid dynamical systems. A major contribution to mathematical system theory and control system theory, this book is written from a system-theoretic point of view with the highest standards of exposition and rigor. It is intended for graduate students, researchers, and practitioners of engineering and applied mathematics as well as computer scientists, physicists, and other scientists who seek a fundamental understanding of the rich dynamical behavior of impulsive and hybrid dynamical systems.

Automatic control. --- Control theory. --- Dynamics. --- Discrete-time systems. --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Dynamics --- Machine theory --- Control engineering --- Control equipment --- Control theory --- Engineering instruments --- Automation --- Programmable controllers --- DES (System analysis) --- Discrete event systems --- Sampled-data systems --- Digital control systems --- Discrete mathematics --- System analysis --- Linear time invariant systems --- Actuator. --- Adaptive control. --- Algorithm. --- Amplitude. --- Analog computer. --- Arbitrarily large. --- Asymptote. --- Asymptotic analysis. --- Axiom. --- Balance equation. --- Bode plot. --- Boundedness. --- Calculation. --- Center of mass (relativistic). --- Coefficient of restitution. --- Continuous function. --- Convex set. --- Differentiable function. --- Differential equation. --- Dissipation. --- Dissipative system. --- Dynamical system. --- Dynamical systems theory. --- Energy. --- Equations of motion. --- Equilibrium point. --- Escapement. --- Euler–Lagrange equation. --- Exponential stability. --- Forms of energy. --- Hamiltonian mechanics. --- Hamiltonian system. --- Hermitian matrix. --- Hooke's law. --- Hybrid system. --- Identity matrix. --- Inequality (mathematics). --- Infimum and supremum. --- Initial condition. --- Instability. --- Interconnection. --- Invariance theorem. --- Isolated system. --- Iterative method. --- Jacobian matrix and determinant. --- Lagrangian (field theory). --- Lagrangian system. --- Lagrangian. --- Likelihood-ratio test. --- Limit cycle. --- Limit set. --- Linear function. --- Linearization. --- Lipschitz continuity. --- Lyapunov function. --- Lyapunov stability. --- Mass balance. --- Mathematical optimization. --- Melting. --- Mixture. --- Moment of inertia. --- Momentum. --- Monotonic function. --- Negative feedback. --- Nonlinear programming. --- Nonlinear system. --- Nonnegative matrix. --- Optimal control. --- Ordinary differential equation. --- Orthant. --- Parameter. --- Partial differential equation. --- Passive dynamics. --- Poincaré conjecture. --- Potential energy. --- Proof mass. --- Quantity. --- Rate function. --- Requirement. --- Robust control. --- Second law of thermodynamics. --- Semi-infinite. --- Small-gain theorem. --- Special case. --- Spectral radius. --- Stability theory. --- State space. --- Stiffness. --- Supply (economics). --- Telecommunication. --- Theorem. --- Transpose. --- Uncertainty. --- Uniform boundedness. --- Uniqueness. --- Vector field. --- Vibration. --- Zeroth (software). --- Zeroth law of thermodynamics.