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Parametric Bootstrap for Linear Regression with Long-memory Errors: An Improvement to the Traditional Delta Method Approach - Brossura
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Invented in 1979 by Bradley Efron, the relatively new topic of bootstrap approximation technique is becoming one of the most efficient and fast expanding methods of statistical analysis, used not only by statisticians, but also by other researchers in economics, finance, medical sciences, life sciences, social sciences, and business. However, the current application of bootstrap is largely focused on independent and identically distributed (iid) data and to a lesser extent on weakly dependent data structures. Very little attempt is done to analyze the performance of bootstrap to strongly dependent (long-memory) processes. This work aims at laying the mathematical foundation for the application of parametric bootstrap to regression processes whose disturbance terms are strongly dependent. It is shown that, under some sets of conditions on the regression coefficients, the spectral density function, and the parameter values, the parametric bootstrap based on the plug-in log-likelihood (PLL) function of linear regression processes with Gaussian, stationary, and long-memory errors, provides higher-order improvements over the traditional delta method.
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Parametric Bootstrap for Linear Regression with Long-memory Errors
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LAP LAMBERT Academic Publishing Jan 2010, 2010
ISBN 10: 3838340612
ISBN 13: 9783838340616
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Invented in 1979 by Bradley Efron, the relatively new topic of bootstrap approximation technique is becoming one of the most efficient and fast expanding methods of statistical analysis, used not only by statisticians, but also by other researchers in economics, finance, medical sciences, life sciences, social sciences, and business. However, the current application of bootstrap is largely focused on independent and identically distributed (iid) data and to a lesser extent on weakly dependent data structures. Very little attempt is done to analyze the performance of bootstrap to strongly dependent (long-memory) processes. This work aims at laying the mathematical foundation for the application of parametric bootstrap to regression processes whose disturbance terms are strongly dependent. It is shown that, under some sets of conditions on the regression coefficients, the spectral density function, and the parameter values, the parametric bootstrap based on the plug-in log-likelihood (PLL) function of linear regression processes with Gaussian, stationary, and long-memory errors, provides higher-order improvements over the traditional delta method. 64 pp. Englisch. Codice articolo 9783838340616
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Parametric Bootstrap for Linear Regression with Long-memory Errors
Editore:
LAP Lambert Academic Publishing, 2010
ISBN 10: 3838340612
ISBN 13: 9783838340616
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Brossura
Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Invented in 1979 by Bradley Efron, the relatively new topic of bootstrap approximation technique is becoming one of the most efficient and fast expanding methods of statistical analysis, used not only by statisticians, but also by other researchers in econo. Codice articolo 5414577
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Parametric Bootstrap for Linear Regression with Long-memory Errors
Editore:
LAP LAMBERT Academic Publishing Jan 2010, 2010
ISBN 10: 3838340612
ISBN 13: 9783838340616
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Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Invented in 1979 by Bradley Efron, the relatively new topic of bootstrap approximation technique is becoming one of the most efficient and fast expanding methods of statistical analysis, used not only by statisticians, but also by other researchers in economics, finance, medical sciences, life sciences, social sciences, and business. However, the current application of bootstrap is largely focused on independent and identically distributed (iid) data and to a lesser extent on weakly dependent data structures. Very little attempt is done to analyze the performance of bootstrap to strongly dependent (long-memory) processes. This work aims at laying the mathematical foundation for the application of parametric bootstrap to regression processes whose disturbance terms are strongly dependent. It is shown that, under some sets of conditions on the regression coefficients, the spectral density function, and the parameter values, the parametric bootstrap based on the plug-in log-likelihood (PLL) function of linear regression processes with Gaussian, stationary, and long-memory errors, provides higher-order improvements over the traditional delta method.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch. Codice articolo 9783838340616
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Parametric Bootstrap for Linear Regression with Long-memory Errors : An Improvement to the Traditional Delta Method Approach
Editore:
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838340612
ISBN 13: 9783838340616
Nuovo
Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Invented in 1979 by Bradley Efron, the relatively new topic of bootstrap approximation technique is becoming one of the most efficient and fast expanding methods of statistical analysis, used not only by statisticians, but also by other researchers in economics, finance, medical sciences, life sciences, social sciences, and business. However, the current application of bootstrap is largely focused on independent and identically distributed (iid) data and to a lesser extent on weakly dependent data structures. Very little attempt is done to analyze the performance of bootstrap to strongly dependent (long-memory) processes. This work aims at laying the mathematical foundation for the application of parametric bootstrap to regression processes whose disturbance terms are strongly dependent. It is shown that, under some sets of conditions on the regression coefficients, the spectral density function, and the parameter values, the parametric bootstrap based on the plug-in log-likelihood (PLL) function of linear regression processes with Gaussian, stationary, and long-memory errors, provides higher-order improvements over the traditional delta method. Codice articolo 9783838340616
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EUR 49,00
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