regression models time series di kedem benjamin (21 risultati)

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Hardcover. Condizione: Fine. Minty copy, bright glossy pictorial boards, clean and unmarked, tight binding.

Condizione: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9780471363552.

Condizione: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9780471363552.

Condizione: new.

Condizione: New.

Condizione: New.

Condizione: New. In.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: new. Hardcover. A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis. Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm * Prediction and interpolation * Stationary processes Regression methods have been an integral part of time series analysis. Developments have made major strides in such areas as non continuous data where a linear model is not appropriate. This is a review of the regression methods in time series analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: new. Hardcover. A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis. Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm * Prediction and interpolation * Stationary processes Regression methods have been an integral part of time series analysis. Developments have made major strides in such areas as non continuous data where a linear model is not appropriate. This is a review of the regression methods in time series analysis. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New. Regression methods have been an integral part of time series analysis. Developments have made major strides in such areas as non continuous data where a linear model is not appropriate. This is a review of the regression methods in time series analysis. Series: Wiley Series in Probability and Statistics. Num Pages: 360 pages, Ill. BIC Classification: PBT; PBWH. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 238 x 166 x 26. Weight in Grams: 670. . 2002. 1st Edition. Hardcover. . . . .

Hardcover. Condizione: Brand New. 1st edition. 320 pages. 9.25x6.00x1.00 inches. In Stock.

Condizione: New. pp. xiv + 337.

Hardcover. Condizione: new. Hardcover. A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis. Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm * Prediction and interpolation * Stationary processes Regression methods have been an integral part of time series analysis. Developments have made major strides in such areas as non continuous data where a linear model is not appropriate. This is a review of the regression methods in time series analysis. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condizione: New. Regression methods have been an integral part of time series analysis. Developments have made major strides in such areas as non continuous data where a linear model is not appropriate. This is a review of the regression methods in time series analysis. Series: Wiley Series in Probability and Statistics. Num Pages: 360 pages, Ill. BIC Classification: PBT; PBWH. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 238 x 166 x 26. Weight in Grams: 670. . 2002. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Buch. Condizione: Neu. Neuware - A thorough review of the most current regression methods in time series analysisRegression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis.Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data.The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements.Notably, the book covers:\* Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling\* Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm\* Prediction and interpolation\* Stationary processes.

Hardcover. Condizione: Brand New. 1st edition. 320 pages. 9.25x6.00x1.00 inches. In Stock. This item is printed on demand.

Condizione: New. pp. xiv + 337 Illus. This item is printed on demand.