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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.

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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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

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In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

multiple cancer types --- integrative analysis --- omics data --- prognosis modeling --- classification --- gene set enrichment analysis --- boosting --- kernel method --- Bayes factor --- Bayesian mixed-effect model --- CpG sites --- DNA methylation --- Ordinal responses --- GEE --- lipid–environment interaction --- longitudinal lipidomics study --- penalized variable selection --- convolutional neural networks --- deep learning --- feed-forward neural networks --- machine learning --- gene regulatory network --- nonparanormal graphical model --- network substructure --- false discovery rate control --- gaussian finite mixture model --- clustering analysis --- uncertainty --- expectation-maximization algorithm --- classification boundary --- gene expression --- RNA-seq --- n/a --- lipid-environment interaction

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Numerical linear algebra is a very important topic in mathematics and has important recent applications in deep learning, machine learning, image processing, applied statistics, artificial intelligence and other interesting modern applications in many fields. The purpose of this Special Issue in Mathematics is to present the latest contributions and recent developments in numerical linear algebra and applications in different real domains. We invite authors to submit original and new papers and high-quality reviews related to the following topics: applied linear algebra, linear and nonlinear systems of equations, large matrix equations, numerical tensor problems with applications, ill-posed problems and image processing, linear algebra and applied statistics, model reduction in dynamic systems, and other related subjects. The submitted papers will be reviewed in line with the traditional submission process. This Special Issue will be dedicated to the inspired mathematician Constantin Petridi, who has devoted his life to mathematics.

Information technology industries --- inverse scattering --- reciprocity gap functional --- chiral media --- mixed boundary conditions --- non-linear matrix equations --- perturbation bounds --- Lyapunov majorants --- fixed-point principle --- nonsymmetric differential matrix Riccati equation --- cosine product --- Golub–Kahan algorithm --- Krylov subspaces --- PCA --- SVD --- tensors --- quadratic form --- estimates --- upper bounds --- networks --- perron vector --- power method --- lanczos method --- pseudospectra --- eigenvalues --- matrix polynomial --- perturbation --- Perron root --- large-scale matrices --- approximation algorithm --- high-dimensional --- minimum norm solution --- regularisation --- Tikhonov --- ℓp-ℓq --- variable selection

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This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques.

Research & information: general --- Geography --- AGB estimation and mapping --- mangroves --- UAV LiDAR --- WorldView-2 --- terrestrial laser scanning --- above-ground biomass --- nondestructive method --- DBH --- bark roughness --- Landsat dataset --- forest AGC estimation --- random forest --- spatiotemporal evolution --- aboveground biomass --- variable selection --- forest type --- machine learning --- subtropical forests --- Landsat 8 OLI --- seasonal images --- stepwise regression --- map quality --- subtropical forest --- urban vegetation --- biomass estimation --- Sentinel-2A --- Xuzhou --- forest biomass estimation --- forest inventory data --- multisource remote sensing --- biomass density --- ecosystem services --- trade-off --- synergy --- multiple ES interactions --- valley basin --- norway spruce --- LiDAR --- allometric equation --- individual tree detection --- tree height --- diameter at breast height --- GEOMON --- ALOS-2 L band SAR --- Sentinel-1 C band SAR --- Sentinel-2 MSI --- ALOS DSM --- stand volume --- support vector machine for regression --- ordinary kriging --- forest succession --- leaf area index --- plant area index --- machine learning algorithms --- forest growing stock volume --- SPOT6 imagery --- Pinus massoniana plantations --- sentinel 2 --- landsat --- remote sensing --- GIS --- shrubs biomass --- bioenergy --- vegetation indices