The issue dedicates to promoting the application of Statistics in Biology, Medicine, Economics, Public Health, Social Statistics and Research.

This issue dedicates to promoting the application of statistical model in biology, public health policy, clinical medicine, health economics, genomics, economics and social statistics, price statistics, statistical classification, economic statistic and research, Commerce and government and development and innovation statistics.

The applications in multivariate methods have increased tremendously in recent years. It is now difficult to cover the variety of real-world applications of these methods. However, in order to give some indications of the usefulness of multivariate techniques, the purpose of this special edition will be to enlighten the areas of research, perspectives and utility of different regression models to predict the physical and mental disabilities of the elderly.

In many fields of knowledge, such as climatic change studies, epidemiology, and meteorology, are of huge interest the ability to model spatial and temporal data. Previously, spatial data and temporal data were analyzed separately. However, due to better statistical models and to higher computational power available in recent years, the modeling of spatial-temporal data is receiving a huge interest lately. Today, bayesian and dynamic models for spatial-temporal data is receiving huge interest.

The purpose of this special issue will be to enlighten the research areas, prospects and usefulness of different forecasting techniques available or need for developing forecasting techniques for efficient forecasting in climate and environment resilient Agriculture and Allied Sector for food and nutritional security throughout the Globe.

Remote sensing has extensively been used for mapping, inventory and natural resource management. It has gain popularity due to its global as well regional coverage of earth’s surface in repetitive and cost effective manner. Remote sensing applications are subjected to statistical methods and analysis to derive meaningful information relating modeling, mapping, monitoring and decision making scenarios. The statistical methods are largely used for image processing and thematic mapping. In image processing, several statistical image enhancement algorithms are used to suppress the noise and to make image more expressive. In addition, spatial statistics techniques are attracting remote sensing community for providing information in spatial format. The multivariate statistics and modeling has vital role in remote sensing applications i.e. biomass estimation, yield estimation, biodiversity estimation, geological studies, hydrological modeling and so on. Predictive modeling is another vibrant aspect of statistics to maintain and save the natural resources and biodiversity through remote sensing data.

The aim of this special issue is to foster state-of-the-art research in the area of remote sensing where statistical methods are prominent in delivering significant meaningful results. This special issue focuses on development and implementation of advanced statistical methodologies in image processing, quantification of environmental parameters, sampling, decision making, final map generation, satellite image classification, accuracy assessment, and so on. This issue will also serve as a milestone source for further motivation in innovation and employment of statistical methods in remote sensing data applications.