2 results (0,15536 seconds)

Brand

Merchant

Price (EUR)

Reset filter

Products
From
Shops

Mixed Effects Models for the Population Approach Models Tasks Methods and Tools

Mixed Effects Models for the Population Approach Models Tasks Methods and Tools

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models Tasks Methods and Tools presents a rigorous framework for describing implementing and using mixed effects models. With these models readers can perform parameter estimation and modeling across a whole population of individuals at the same time. Easy-to-Use Techniques and Tools for Real-World Data ModelingThe book first shows how the framework allows model representation for different data types including continuous categorical count and time-to-event data. This leads to the use of generic methods such as the stochastic approximation of the EM algorithm (SAEM) for modeling these diverse data types. The book also covers other essential methods including Markov chain Monte Carlo (MCMC) and importance sampling techniques. The author uses publicly available software tools to illustrate modeling tasks. Methods are implemented in Monolix and models are visually explored using Mlxplore and simulated using Simulx. Careful Balance of Mathematical Representation and Practical ImplementationThis book takes readers through the whole modeling process from defining/creating a parametric model to performing tasks on the model using various mathematical methods. Statisticians and mathematicians will appreciate the rigorous representation of the models and theoretical properties of the methods while modelers will welcome the practical capabilities of the tools. The book is also useful for training and teaching in any field where population modeling occurs. | Mixed Effects Models for the Population Approach Models Tasks Methods and Tools

GBP 42.99
1

Statistical Methods for Spatial Data Analysis

Statistical Methods for Spatial Data Analysis

Understanding spatial statistics requires tools from applied and mathematical statistics linear model theory regression time series and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data. This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression providing a detailed development of linear models with uncorrelated errors linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields non-stationary covariance and spatio-temporal processes. Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text software code that can be used to implement many of the principal methods described and illustrated and updates to the text itself.

GBP 42.99
1