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As a byproduct of historical development, there are different, unrelated systems of nomenclature for ""inorganic chemistry"", ""organic chemistry"", ""polymer chemistry"", ""natural products chemistry"", etc. With each new discovery in the laboratory, as well as each new theoretical proposal for a chemical, the lines that traditionally have separated these ""distinct"" subsets of matter continually grow more blurred. This lack of uniformity in characterizing and naming chemicals increases the communication difficulties between differently trained chemists, as well as other scientists, and gr
Chemistry --- Parameter estimation --- System identification --- System identification. --- Parameter estimation.
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Parameter estimation --- Regression Analysis --- Optimal designs (Statistics)
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Mathematics --- Parameter estimation. --- Inverse problems (Differential equations) --- Mathematical models
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Bayesian Bounds provides a collection of the important papers dealing with the theory and application of Bayesian bounds. The book is essential to both engineers and statisticians whether they are practitioners or theorists. Each part of the book is introduced with the contributions of each selected paper and their interrelationship.
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Mathematical statistics --- Control theory --- Parameter estimation --- Theorie du controle --- Controle stochastique
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This volume consists of a collection of research articles on classical and emerging Statistical Paradigms - parametric, non-parametric and semi-parametric, frequentist and Bayesian - encompassing both theoretical advances and emerging applications in a variety of scientific disciplines. For advances in theory, the topics include: Bayesian Inference, Directional Data Analysis, Distribution Theory, Econometrics and Multiple Testing Procedures. The areas in emerging applications include: Bioinformatics, Factorial Experiments and Linear Models, Hotspot Geoinformatics and Reliability.
Parameter estimation. --- Estimation theory. --- Estimating techniques --- Least squares --- Mathematical statistics --- Stochastic processes --- Estimation theory --- Stochastic systems
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This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.
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This collection brings together important contributions by leading econometricians on (i) parametric approaches to qualitative and sample selection models, (ii) nonparametric and semi-parametric approaches to qualitative and sample selection models, and (iii) nonlinear estimation of cross-sectional and time series models. The advances achieved here can have important bearing on the choice of methods and analytical techniques in applied research.
Econometric models --- Nonlinear theories --- Parameter estimation --- Sampling (Statistics) --- Congresses --- Business, Economy and Management --- Economics --- Estimation theory --- Stochastic systems --- Econometric models - Congresses --- Nonlinear theories - Congresses --- Parameter estimation - Congresses --- Sampling (Statistics) - Congresses
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