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This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics.

scene classification --- teacher-student --- noisy labels --- knowledge distillation --- remote sensing images --- LightGBM --- spatiotemporal weight interpolation --- AOD recovery --- East Asia --- polarized SAR --- optical image --- random forest --- conditional random fields --- feature-level fusion --- Dirichlet process --- infinite mixture models --- Gamma distribution --- variational inference --- online setting --- oil spill detection --- synthetic aperture radar images --- GNSS-R --- CYGNSS --- high wind speed inversion --- SVR --- PCA-SVR --- CNN --- n/a

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The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes. In Models for Ecological Data, James Clark introduces ecologists to these modern methods in modeling and computation. Assuming only basic courses in calculus and statistics, the text introduces readers to basic maximum likelihood and then works up to more advanced topics in Bayesian modeling and computation. Clark covers both classical statistical approaches and powerful new computational tools and describes how complexity can motivate a shift from classical to Bayesian methods. Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R. Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to develop useful models that make sense of ecological data. Consistent treatment from classical to modern Bayes Underlying distribution theory to algorithm development Many examples and applications Does not assume statistical background Extensive supporting appendixes Accompanying lab manual in R

Environmental sciences --- Ecology --- Mathematical models. --- Mathematical models. --- Dirichlet distribution. --- Fisher Information. --- Hadamard product. --- Poisson. --- Weibull distribution. --- autocorrelation. --- autocovariance. --- beta distribution. --- beta-binomial. --- binomial distribution. --- completing the square. --- confidence interval. --- correlation. --- covariance. --- differential equation. --- eigenanalysis. --- exponential distribution. --- extreme value distribution. --- fecundity. --- frequentist. --- gamma distribution. --- generation time. --- integrated analysis. --- inverse gamma. --- kriging. --- logistic population growth. --- longitudinal model. --- multinomial. --- negative binomial. --- positive definite matrix. --- predictive loss. --- spectral density. --- stage structured model. --- uniform distribution.

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The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results. The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.

519.246 --- Time-series analysis --- modeles economiques --- AA / International- internationaal --- 303.0 --- 304.0 --- 306.5 --- 519.55 --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Mathematical statistics --- Probabilities --- Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- economische modellen --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken). --- Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen. --- Statistische analyse (methodologie). --- 519.246 Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- Time-series analysis. --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken) --- Zuivere statistische analyse (algemene naslagwerken). Tijdreeksen --- Statistische analyse (methodologie) --- Stochastic processes --- Statistical science --- Série chronologique --- Absolute summability. --- Autocovariance. --- Bartlett kernel. --- Block exogeneity. --- Cointegrating vector. --- Consumption spending. --- Cospectrum. --- Dickey-Fuller test. --- EM algorithm. --- Exchange rates. --- Filters. --- Fundamental innovation. --- Gamma distribution. --- Global identification. --- Gross national product. --- Hessian matrix. --- Inequality constraints. --- Invertibility. --- Jacobian matrix. --- Joint density. --- Khinchine's theorem. --- Kronecker product. --- Lagrange multiplier. --- Loss function. --- Mean-value theorem. --- Mixingales. --- Monte Carlo method. --- Newton-Raphson. --- Order in probability. --- Orthogonal. --- Permanent income. --- Quadrature spectrum. --- Recessions. --- Reduced form. --- Sample periodogram. --- Stock prices. --- Taylor series. --- Vech operator. --- Time series analysis

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Most papers published in this Special Issue of Mathematics are written by the participants of the XXXVI International Seminar on Stability Problems for Stochastic Models, 21­25 June, 2021, Petrozavodsk, Russia. The scope of the seminar embraces the following topics: Limit theorems and stability problems; Asymptotic theory of stochastic processes; Stable distributions and processes; Asymptotic statistics; Discrete probability models; Characterization of probability distributions; Insurance and financial mathematics; Applied statistics; Queueing theory; and other fields. This Special Issue contains 12 papers by specialists who represent 6 countries: Belarus, France, Hungary, India, Italy, and Russia.

inhomogeneous continuous-time Markov chain --- weak ergodicity --- rate of convergence --- sharp bounds --- differential inequalities --- forward Kolmogorov system --- prefetching --- optimization --- Markov decision processes --- random trees --- Galton–Watson --- capacitance --- dirichlet boundary value problem --- monte carlo method --- unbiased estimator --- von-neumann-ulam scheme --- network evolution --- random graph --- multi-type branching process --- continuous-time branching process --- 2- and 3-interactions --- Malthusian parameter --- Poisson process --- life-length --- extinction --- queuing system --- elastic traffic --- inpatient claim --- non-stationary intensity --- convergence analysis --- bounds on the rate of convergence --- wireless network --- file transfer --- daily traffic profile --- blocking probability --- continuous-time ehrenfest model --- first-passage time densities --- proportional intensity functions --- asymptotic behaviors --- multi-server queueing model --- rating --- self-sufficient servers --- self-checkout --- assistants --- multi-dimensional Markov chains --- retrial queue --- negative customers --- resource heterogeneous queue --- asymptotic analysis --- discrete time functional filter --- optimal unbiased estimation --- steady state --- equilibrium arrivals --- one-server queueing system --- orbit --- retrials --- limit theorem --- sum of independent random variables --- random sum --- asymptotic expansion --- asymptotic deficiency --- kurtosis --- parameter estimation --- gamma-exponential distribution --- mixed distributions --- generalized gamma distribution --- generalized beta distribution --- method of moments --- cumulants --- asymptotic normality

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Entropy theory has wide applications to a range of problems in the fields of environmental and water engineering, including river hydraulic geometry, fluvial hydraulics, water monitoring network design, river flow forecasting, floods and droughts, river network analysis, infiltration, soil moisture, sediment transport, surface water and groundwater quality modeling, ecosystems modeling, water distribution networks, environmental and water resources management, and parameter estimation. Such applications have used several different entropy formulations, such as Shannon, Tsallis, Reacutenyi Burg, Kolmogorov, Kapur, configurational, and relative entropies, which can be derived in time, space, or frequency domains. More recently, entropy-based concepts have been coupled with other theories, including copula and wavelets, to study various issues associated with environmental and water resources systems. Recent studies indicate the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering, including establishing and explaining physical connections between theory and reality. The objective of this Special Issue is to provide a platform for compiling important recent and current research on the applications of entropy theory in environmental and water engineering. The contributions to this Special Issue have addressed many aspects associated with entropy theory applications and have shown the enormous scope and potential of entropy theory in advancing research in the fields of environmental and water engineering.

hydrological risk analysis --- modeling --- water level --- Poyang Lake basin --- trend --- composite multiscale sample entropy --- flood frequency analysis --- canopy flow --- precipitation --- water resources --- complex systems --- frequency analysis --- optimization --- combined forecast --- neural network forecast --- entropy spectral analysis time series analysis --- environmental engineering --- hydrometric network --- sea surface temperature --- kernel density estimation --- robustness --- turbulent flow --- entropy production --- connection entropy --- flux concentration relation --- turbulence --- tropical rainfall --- generalized gamma (GG) distribution --- multi-events --- El Niño --- joint entropy --- entropy weighting method --- Anhui Province --- changing environment --- complexity --- multiplicative cascades --- Tsallis entropy --- Hexi corridor --- coherent structures --- water resources vulnerability --- uncertainty --- variability --- flow entropy --- Hei River basin --- fuzzy analytic hierarchy process --- substitute --- crop yield --- conditional entropy production --- entropy --- flow duration curve --- mean annual runoff --- temperature --- hydrometeorological extremes --- resilience --- Loess Plateau --- information entropy --- scaling --- water distribution networks --- cross entropy --- randomness --- forewarning model --- entropy applications --- quaternary catchment --- spatio-temporal variability --- probability distribution function --- ant colony fuzzy clustering --- radar --- continuous probability distribution functions --- Shannon entropy --- informational entropy --- information --- confidence intervals --- marginal entropy --- rainfall forecast --- entropy of information --- streamflow --- power laws --- bootstrap aggregating --- maximum entropy-copula method --- spatial and dynamics characteristic --- projection pursuit --- set pair analysis --- entropy theory --- water resource carrying capacity --- entropy parameter --- precipitation frequency analysis --- principle of maximum entropy --- information theory --- stochastic processes --- network design --- complement --- cross elasticity --- climacogram --- methods of moments --- hydrology --- bagging --- principle of maximum entropy (POME) --- rainfall network --- entropy ensemble filter --- ensemble model simulation criterion --- Lagrangian function --- Beta-Lognormal model --- cross-entropy minimization --- ANN --- configurational entropy --- variation of information --- statistical scaling --- EEF method --- water monitoring --- maximum likelihood estimation --- GB2 distribution --- NDVI --- four-parameter exponential gamma distribution --- hydraulics --- spatial optimization --- Kolmogorov complexity --- bootstrap neural networks --- mutual information --- accelerating genetic algorithm --- groundwater depth --- rainfall --- tropical Pacific --- water engineering --- monthly streamflow forecasting --- ENSO --- nonlinear relation --- Bayesian technique --- non-point source pollution --- Burg entropy --- data-scarce --- scaling laws --- soil water content --- arid region --- land suitability evaluation --- information transfer