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This book addresses contemporary statistical inference issues when no or minimal assumptions on the nature of studied phenomenon are imposed. Information theory methods play an important role in such scenarios. The approaches discussed include various high-dimensional regression problems, time series and dependence analyses.

Technology: general issues --- History of engineering & technology --- Mechanical engineering & materials --- high-dimensional time series --- nonstationarity --- network estimation --- change points --- kernel estimation --- high-dimensional regression --- loss function --- random predictors --- misspecification --- consistent selection --- subgaussianity --- generalized information criterion --- robustness --- statistical learning theory --- information theory --- entropy --- parameter estimation --- learning systems --- privacy --- prediction methods --- misclassification risk --- model misspecification --- penalized estimation --- supervised classification --- variable selection consistency --- archimedean copula --- consistency --- estimation --- extreme-value copula --- tail dependency --- multivariate analysis --- conditional mutual information --- CMI --- information measures --- nonparametric variable selection criteria --- gaussian mixture --- conditional infomax feature extraction --- CIFE --- joint mutual information criterion --- JMI --- generative tree model --- Markov blanket --- minimum distance estimation --- maximum likelihood estimation --- influence functions --- adaptive splines --- B-splines --- right-censored data --- semiparametric regression --- synthetic data transformation --- time series --- n/a

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The description for this book, Linear Inequalities and Related Systems. (AM-38), Volume 38, will be forthcoming.

Operational research. Game theory --- Linear programming. --- Matrices. --- Game theory. --- Games, Theory of --- Theory of games --- Mathematical models --- Mathematics --- Algebra, Matrix --- Cracovians (Mathematics) --- Matrix algebra --- Matrixes (Algebra) --- Algebra, Abstract --- Algebra, Universal --- Production scheduling --- Programming (Mathematics) --- Banach space. --- Basic solution (linear programming). --- Big O notation. --- Bilinear form. --- Boundary (topology). --- Brouwer fixed-point theorem. --- Characterization (mathematics). --- Coefficient. --- Combination. --- Computation. --- Computational problem. --- Convex combination. --- Convex cone. --- Convex hull. --- Convex set. --- Corollary. --- Correlation and dependence. --- Cramer's rule. --- Cyclic permutation. --- Dedekind cut. --- Degeneracy (mathematics). --- Determinant. --- Diagram (category theory). --- Dilworth's theorem. --- Dimension (vector space). --- Directional derivative. --- Disjoint sets. --- Doubly stochastic matrix. --- Dual space. --- Duality (mathematics). --- Duality (optimization). --- Eigenvalues and eigenvectors. --- Elementary proof. --- Equation solving. --- Equation. --- Equivalence class. --- Euclidean space. --- Existence theorem. --- Existential quantification. --- Extreme point. --- Fixed-point theorem. --- Functional analysis. --- Fundamental theorem. --- General equilibrium theory. --- Hall's theorem. --- Hilbert space. --- Incidence matrix. --- Inequality (mathematics). --- Infimum and supremum. --- Invertible matrix. --- Kakutani fixed-point theorem. --- Lagrange multiplier. --- Linear equation. --- Linear inequality. --- Linear map. --- Linear space (geometry). --- Linear subspace. --- Loss function. --- Main diagonal. --- Mathematical induction. --- Mathematical optimization. --- Mathematical problem. --- Max-flow min-cut theorem. --- Maxima and minima. --- Maximal set. --- Maximum flow problem. --- Menger's theorem. --- Minor (linear algebra). --- Monotonic function. --- N-vector. --- Nonlinear programming. --- Nonnegative matrix. --- Parity (mathematics). --- Partially ordered set. --- Permutation matrix. --- Permutation. --- Polyhedron. --- Quantity. --- Representation theorem. --- Row and column vectors. --- Scientific notation. --- Sensitivity analysis. --- Set notation. --- Sign (mathematics). --- Simplex algorithm. --- Simultaneous equations. --- Solution set. --- Special case. --- Subset. --- Summation. --- System of linear equations. --- Theorem. --- Transpose. --- Unit sphere. --- Unit vector. --- Upper and lower bounds. --- Variable (mathematics). --- Vector space. --- Von Neumann's theorem.

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The Special Issue entitled “Remote Sensing in Vessel Detection and Navigation” comprises 15 articles on many topics related to remote sensing with navigational sensors. The sequence of articles included in this Special Issue is in line with the latest scientific trends. The latest developments in science, including artificial intelligence, were used. It can be said that navigation and vessel detection remain important and hot topics, and a lot of work will continue to be done worldwide. New techniques and methods for analyzing and extracting information from navigational sensors and data have been proposed and verified. Some of these will spark further research, and some are already mature and can be considered for industrial implementation and development.

Research & information: general --- autonomous navigation --- automatic radar plotting aid --- safe objects control --- game theory --- computer simulation --- Sentinel-2 --- multispectral --- temporal offsets --- ship --- aircraft --- velocity --- altitude --- parallax --- jet stream --- Unmanned Surface Vessel (USV) --- multi-Global Navigation Satellite System (GNSS) receiver --- bathymetric measurements --- cross track error (XTE) --- SSL --- six-degrees-of-freedom motion --- motion attitude model --- edge detection --- straight-line fitting --- visual saliency --- vessel detection --- video monitoring --- inland waterway --- real-time detection --- neural network --- target recognition --- HRRP --- residual structure --- loss function --- trajectory tracking --- unmanned surface vehicle --- navigation --- bathymetry --- hydrographic survey --- real-time communication --- maritime situational awareness --- ship detection --- Iridium --- on-board --- image processing --- flight campaign --- position estimation --- ranging mode --- single shore station --- AIS --- bag-of-words mechanism --- machine learning --- image analysis --- ship classification --- marine system --- river monitoring system --- feature extraction --- synthetic aperture radar (SAR) ship detection --- multi-stage rotational region based network (MSR2N) --- rotated anchor generation --- multi-stage rotational detection network (MSRDN) --- convolutional neural network (CNN) --- synthetic aperture radar (SAR) --- multiscale and small ship detection --- complex background --- false alarm --- farbon dioxide peaks --- midwave infrared --- FTIR --- adaptive stochastic resonance (ASR) --- matched intrawell response --- nonlinear filter --- line enhancer --- autonomous underwater vehicles (AUVs) --- target tracking --- group targets --- GLMB --- structure --- formation --- remote sensing

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The Special Issue entitled “Remote Sensing in Vessel Detection and Navigation” comprises 15 articles on many topics related to remote sensing with navigational sensors. The sequence of articles included in this Special Issue is in line with the latest scientific trends. The latest developments in science, including artificial intelligence, were used. It can be said that navigation and vessel detection remain important and hot topics, and a lot of work will continue to be done worldwide. New techniques and methods for analyzing and extracting information from navigational sensors and data have been proposed and verified. Some of these will spark further research, and some are already mature and can be considered for industrial implementation and development.

Research & information: general --- autonomous navigation --- automatic radar plotting aid --- safe objects control --- game theory --- computer simulation --- Sentinel-2 --- multispectral --- temporal offsets --- ship --- aircraft --- velocity --- altitude --- parallax --- jet stream --- Unmanned Surface Vessel (USV) --- multi-Global Navigation Satellite System (GNSS) receiver --- bathymetric measurements --- cross track error (XTE) --- SSL --- six-degrees-of-freedom motion --- motion attitude model --- edge detection --- straight-line fitting --- visual saliency --- vessel detection --- video monitoring --- inland waterway --- real-time detection --- neural network --- target recognition --- HRRP --- residual structure --- loss function --- trajectory tracking --- unmanned surface vehicle --- navigation --- bathymetry --- hydrographic survey --- real-time communication --- maritime situational awareness --- ship detection --- Iridium --- on-board --- image processing --- flight campaign --- position estimation --- ranging mode --- single shore station --- AIS --- bag-of-words mechanism --- machine learning --- image analysis --- ship classification --- marine system --- river monitoring system --- feature extraction --- synthetic aperture radar (SAR) ship detection --- multi-stage rotational region based network (MSR2N) --- rotated anchor generation --- multi-stage rotational detection network (MSRDN) --- convolutional neural network (CNN) --- synthetic aperture radar (SAR) --- multiscale and small ship detection --- complex background --- false alarm --- farbon dioxide peaks --- midwave infrared --- FTIR --- adaptive stochastic resonance (ASR) --- matched intrawell response --- nonlinear filter --- line enhancer --- autonomous underwater vehicles (AUVs) --- target tracking --- group targets --- GLMB --- structure --- formation --- remote sensing

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Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.

Robust optimization. --- Linear programming. --- 519.8 --- 681.3*G16 --- 681.3*G16 Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- 519.8 Operational research --- Operational research --- Robust optimization --- Linear programming --- Optimisation robuste --- Programmation linéaire --- Optimization, Robust --- RO (Robust optimization) --- Mathematical optimization --- Production scheduling --- Programming (Mathematics) --- 0O. --- Accuracy and precision. --- Additive model. --- Almost surely. --- Approximation algorithm. --- Approximation. --- Best, worst and average case. --- Bifurcation theory. --- Big O notation. --- Candidate solution. --- Central limit theorem. --- Chaos theory. --- Coefficient. --- Computational complexity theory. --- Constrained optimization. --- Convex hull. --- Convex optimization. --- Convex set. --- Cumulative distribution function. --- Curse of dimensionality. --- Decision problem. --- Decision rule. --- Degeneracy (mathematics). --- Diagram (category theory). --- Duality (optimization). --- Dynamic programming. --- Exponential function. --- Feasible region. --- Floor and ceiling functions. --- For All Practical Purposes. --- Free product. --- Ideal solution. --- Identity matrix. --- Inequality (mathematics). --- Infimum and supremum. --- Integer programming. --- Law of large numbers. --- Likelihood-ratio test. --- Linear dynamical system. --- Linear inequality. --- Linear map. --- Linear matrix inequality. --- Linear regression. --- Loss function. --- Margin classifier. --- Markov chain. --- Markov decision process. --- Mathematical optimization. --- Max-plus algebra. --- Maxima and minima. --- Multivariate normal distribution. --- NP-hardness. --- Norm (mathematics). --- Normal distribution. --- Optimal control. --- Optimization problem. --- Orientability. --- P versus NP problem. --- Pairwise. --- Parameter. --- Parametric family. --- Probability distribution. --- Probability. --- Proportionality (mathematics). --- Quantity. --- Random variable. --- Relative interior. --- Robust control. --- Robust decision-making. --- Semi-infinite. --- Sensitivity analysis. --- Simple set. --- Singular value. --- Skew-symmetric matrix. --- Slack variable. --- Special case. --- Spherical model. --- Spline (mathematics). --- State variable. --- Stochastic calculus. --- Stochastic control. --- Stochastic optimization. --- Stochastic programming. --- Stochastic. --- Strong duality. --- Support vector machine. --- Theorem. --- Time complexity. --- Uncertainty. --- Uniform distribution (discrete). --- Unimodality. --- Upper and lower bounds. --- Variable (mathematics). --- Virtual displacement. --- Weak duality. --- Wiener filter. --- With high probability. --- Without loss of generality.