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Extinction and Quasi-Stationarity in the Stochastic Logistic SIS Model

and from quasi-stationarity for the stochastic logistic SIS model. The approximations are derived separately in three different parameter;

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Modeling in Mathematics

, dynamical analysis and chaos control of a discrete sis epidemic model, electricity market stochastic dynamic model and its mean stability analysis;

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Stochastic Processes

, Variance, Covariance and Correlation. It contains also how to work with Stochastic Processes Strictly or Wide-sense stationarity, with or without;

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Practical Guide to Logistic Regression

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly;

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Practical Guide to Logistic Regression

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly;

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Stochastic Dynamics of Economic Cycles

of model factors. All of the above constitute the general principles of a theory explaining the phenomenon of economic cycles and;

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Selected Aspects of Fractional Brownian Motion

classically used in probability theory. As a centered Gaussian process, it is characterized by the stationarity of its increments and a medium- or;

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Selected Aspects of Fractional Brownian Motion

classically used in probability theory. As a centered Gaussian process, it is characterized by the stationarity of its increments and a medium- or;

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Stochastic Model Checking

and communication protocols which form the structure of the Internet. Stochastic model checking is an important field in stochastic analysis;

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Logistic Regression

In this text, author Scott Menard provides coverage of not only the basic logistic regression model but also advanced topics found in no;

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Random Measures, Theory and Applications

properties of Poisson and related processes to the modern theories of convergence, stationarity, Palm measures, conditioning, and compensation. The;

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Deterministic, Stochastic and Thermodynamic Modelling of some Interacting Species

environment, the influence of environmental noise in Gompertzian and logistic growth models, stability behaviour in randomly fluctuating versus;

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Probability and Stochastic Processes for Physicists

This book seeks to bridge the gap between the parlance, the models, and even the notations used by physicists and those used by;

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Probability and Stochastic Processes for Physicists

This book seeks to bridge the gap between the parlance, the models, and even the notations used by physicists and those used by;

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Semimartingales and their Statistical Inference

Quasi-likelihood Likelihood and efficiency Inference for counting processes Inference for semimartingale regression models The;

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Semimartingales and Their Statistical Inference

Quasi-likelihood Likelihood and efficiency Inference for counting processes Inference for semimartingale regression models The;

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Logistic Regression Models

logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data. Examples;

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Logistic Regression Models

logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data. Examples;

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Applied Logistic Regression

A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded;

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Stochastic Modeling

minimal models that have been used historically to develop new mathematical techniques in the field of stochastic processes: the logistic growth;

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Simulation and Inference for Stochastic Processes with Yuima: A Comprehensive R Framework for Sdes and Other Stochastic Processes

The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic;

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Logistic Regression Using The Sas System

understanding of the material. He also explores the differences and similarities among the many generalizations of the logistic regression model.;

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Numerical Methods For Stochastic Processes

new methods. Topics presented include Monte Carlo and quasi-Monte Carlo methods, the simulation of major stochastic processes and;

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Analytically Tractable Stochastic Stock Price Models

stochastic volatility, and in the Heston model a Cox-Ingersoll-Ross process governs the behavior of the volatility. One of the author's main goals;

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Stochastic Differential Equations

of the mathematical essentials needed to understand and apply stochastic growth models. In addition, the book describes deterministic and;

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Supersymmetric Methods in Quantum and Statistical Physics

examines the classical version and the quantum version of Witten's model. Exact spectral properties for the so-called shape invariant potentials;

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Quasi-Monte Carlo Methods in Finance

Quasi-Monte Carlo simulation of stochastic differential equations, we find them to dominate plain Monte Carlo methods. However, the theoretical;

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