Relevant bibliographies by topics / Durbin-Watson statistic

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Academic literature on the topic 'Durbin-Watson statistic'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Durbin-Watson statistic"

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Champion, R., C. T. Lenard, and T. M. Mills. "Demonstrating the Durbin-Watson Statistic." Journal of the Royal Statistical Society: Series D (The Statistician) 47, no. 4 (December 1998): 643–44. http://dx.doi.org/10.1111/1467-9884.00161.

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Srivastava, M. S. "Asymptotic distribution of Durbin-Watson statistic." Economics Letters 24, no. 2 (January 1987): 157–60. http://dx.doi.org/10.1016/0165-1765(87)90243-6.

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Inder, Brett. "An Approximation to the Null Distribution of the Durbin-Watson Statistic in Models Containing Lagged Dependent Variables." Econometric Theory 2, no. 3 (December 1986): 413–28. http://dx.doi.org/10.1017/s0266466600011683.

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We consider testing for autoregressive disturbances in the linear regression model with a lagged dependent variable. An approximation to the null distribution of the Durbin—Watson statistic is developed using small-disturbance asymptotics, and is used to obtain test critical values. We also obtain nonsimilar critical values for the Durbin—Watson and Durbin's h and t tests. Monte Carlo results are reported comparing the performances of the tests under the null and alternative hypotheses. The Durbin–Watson test is found to be more powerful and to perform more consistently than either of Durbin's tests under Ho.
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Nakamura, Shisei, and Masanobu Taniguchi. "ASYMPTOTIC THEORY FOR THE DURBIN–WATSON STATISTIC UNDER LONG-MEMORY DEPENDENCE." Econometric Theory 15, no. 6 (December 1999): 847–66. http://dx.doi.org/10.1017/s0266466699156044.

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In time series regression models with “short-memory” residual processes, the Durbin–Watson statistic (DW) has been used for the problem of testing for independence of the residuals. In this paper we elucidate the asymptotics of DW for “long-memory” residual processes. A standardized Durbin–Watson statistic (SDW) is proposed. Then we derive the asymptotic distributions of SDW under both the null and local alternative hypotheses. Based on this result we evaluate the local power of SDW. Numerical studies for DW and SDW are given.
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Sheehan, Dennis. "Approximating the distribution of the durbin-watson statistic." Communications in Statistics - Theory and Methods 15, no. 1 (1986): 73–88. http://dx.doi.org/10.1080/03610928608829107.

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Bhargava, Alok. "Missing Observations and the Use of the Durbin-Watson Statistic." Biometrika 76, no. 4 (December 1989): 828. http://dx.doi.org/10.2307/2336649.

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Rutledge, D. N., and A. S. Barros. "Durbin–Watson statistic as a morphological estimator of information content." Analytica Chimica Acta 454, no. 2 (March 2002): 277–95. http://dx.doi.org/10.1016/s0003-2670(01)01555-0.

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BHARGAVA, ALOK. "Missing observations and the use of the Durbin–Watson statistic." Biometrika 76, no. 4 (1989): 828–31. http://dx.doi.org/10.1093/biomet/76.4.828.

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Tsay, W. "On the power of durbin-watson statistic against fractionally integrated processes." Econometric Reviews 17, no. 4 (January 1998): 361–86. http://dx.doi.org/10.1080/07474939808800423.

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Lieberman, Offer. "On the Approximation of Saddlepoint Expansions in Statistics." Econometric Theory 10, no. 5 (December 1994): 900–916. http://dx.doi.org/10.1017/s0266466600008914.

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The saddlepoint approximation as developed by Daniels [3] is an extremely accurate method for approximating probability distributions. Econometric and statistical applications of the technique to densities of statistics of interest are often hindered by the requirements of explicit knowledge of the c.g.f. and the need to obtain an analytical solution to the saddlepoint defining equation. In this paper, we show the conditions under which any approximation to the saddlepoint is justified and suggest a convenient solution. We illustrate with an approximate saddlepoint expansion of the Durbin-Watson test statistic.
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Dissertations / Theses on the topic "Durbin-Watson statistic"

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Huh, Ji Young. "Applications of Monte Carlo Methods in Statistical Inference Using Regression Analysis." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/cmc_theses/1160.

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This paper studies the use of Monte Carlo simulation techniques in the field of econometrics, specifically statistical inference. First, I examine several estimators by deriving properties explicitly and generate their distributions through simulations. Here, simulations are used to illustrate and support the analytical results. Then, I look at test statistics where derivations are costly because of the sensitivity of their critical values to the data generating processes. Simulations here establish significance and necessity for drawing statistical inference. Overall, the paper examines when and how simulations are needed in studying econometric theories.
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Bitseki, Penda Siméon Valère. "Inégalités de déviations, principe de déviations modérées et théorèmes limites pour des processus indexés par un arbre binaire et pour des modèles markoviens." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2012. http://tel.archives-ouvertes.fr/tel-00822136.

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Le contrôle explicite de la convergence des sommes convenablement normalisées de variables aléatoires, ainsi que l'étude du principe de déviations modérées associé à ces sommes constituent les thèmes centraux de cette thèse. Nous étudions principalement deux types de processus. Premièrement, nous nous intéressons aux processus indexés par un arbre binaire, aléatoire ou non. Ces processus ont été introduits dans la littérature afin d'étudier le mécanisme de la division cellulaire. Au chapitre 2, nous étudions les chaînes de Markov bifurcantes. Ces chaînes peuvent être vues comme une adaptation des chaînes de Markov "usuelles'' dans le cas où l'ensemble des indices à une structure binaire. Sous des hypothèses d'ergodicité géométrique uniforme et non-uniforme d'une chaîne de Markov induite, nous fournissons des inégalités de déviations et un principe de déviations modérées pour les chaînes de Markov bifurcantes. Au chapitre 3, nous nous intéressons aux processus bifurcants autorégressifs d'ordre p (). Ces processus sont une adaptation des processus autorégressifs linéaires d'ordre p dans le cas où l'ensemble des indices à une structure binaire. Nous donnons des inégalités de déviations, ainsi qu'un principe de déviations modérées pour les estimateurs des moindres carrés des paramètres "d'autorégression'' de ce modèle. Au chapitre 4, nous traitons des inégalités de déviations pour des chaînes de Markov bifurcantes sur un arbre de Galton-Watson. Ces chaînes sont une généralisation de la notion de chaînes de Markov bifurcantes au cas où l'ensemble des indices est un arbre de Galton-Watson binaire. Elles permettent dans le cas de la division cellulaire de prendre en compte la mort des cellules. Les hypothèses principales que nous faisons dans ce chapitre sont : l'ergodicité géométrique uniforme d'une chaîne de Markov induite et la non-extinction du processus de Galton-Watson associé. Au chapitre 5, nous nous intéressons aux modèles autorégressifs linéaires d'ordre 1 ayant des résidus corrélés. Plus particulièrement, nous nous concentrons sur la statistique de Durbin-Watson. La statistique de Durbin-Watson est à la base des tests de Durbin-Watson, qui permettent de détecter l'autocorrélation résiduelle dans des modèles autorégressifs d'ordre 1. Nous fournissons un principe de déviations modérées pour cette statistique. Les preuves du principe de déviations modérées des chapitres 2, 3 et 4 reposent essentiellement sur le principe de déviations modérées des martingales. Les inégalités de déviations sont établies principalement grâce à l'inégalité d'Azuma-Bennet-Hoeffding et l'utilisation de la structure binaire des processus. Le chapitre 5 est né de l'importance qu'a l'ergodicité explicite des chaînes de Markov au chapitre 3. L'ergodicité géométrique explicite des processus de Markov à temps discret et continu ayant été très bien étudiée dans la littérature, nous nous sommes penchés sur l'ergodicité sous-exponentielle des processus de Markov à temps continu. Nous fournissons alors des taux explicites pour la convergence sous exponentielle d'un processus de Markov à temps continu vers sa mesure de probabilité d'équilibre. Les hypothèses principales que nous utilisons sont : l'existence d'une fonction de Lyapunov et d'une condition de minoration. Les preuves reposent en grande partie sur la construction du couplage et le contrôle explicite de la queue du temps de couplage.
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Huang, Kai-Yi, and 黃凱翊. "Integrating Durbin-Watson Statistic and Taguchi Method For Independent Component Selection." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/03410700979016551116.

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碩士
朝陽科技大學
工業工程與管理系碩士班
101
Independent Component Analysis (ICA) is a multivariate technique aims at linearly transforming correlated variables into independent component. The transforming procedures include whitening, non-Gaussian and component selection. Recently, ICA has been widely used for monitoring non-Gaussian processes. However, the process fault detectability strongly dependents on the selected components. Traditionally, the Euclidean’s L2 norm and Durbin-Watson (DW) statistic were applied for opting the significant IC components. However, the main drawback of both methods still needs engineers’ effort to select the significant IC components. In this study, a simple and objective method will be developed to address the problem. This study proposed to integrate DW statistic and Taguchi method. First of all, the Orthogonal Array (OA) is adopted to experiment several possible combinations of selected components. After that, the DW embedded Signal-to-Noise (SN) ratio is used to measure the experiment results and ultimately in a bid to select significant IC components. The efficiency of the proposed method will be verified via three examples that included a non-Gaussian simulated process and two real case studies that came from Taiwan Power Company and Tennessee Eastman process, respectively. Experiments demonstrated that the proposed method can use lesser ICs to achieve superior performance.
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Books on the topic "Durbin-Watson statistic"

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Srivastava, M. S. Tail probability approximations of a general statistic with application to Durbin-Watson statistic. Toronto: University of Toronto, Dept. of Statistics, 1988.

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Srivastava, M. S. Bootstrapping Durbin-Watson statistics. Toronto: University of Toronto, Dept. of Statistics, 1985.

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Book chapters on the topic "Durbin-Watson statistic"

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MacKinnon, James G. "Durbin-Watson Statistic." In The New Palgrave Dictionary of Economics, 1–3. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2200-1.

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MacKinnon, James G. "Durbin-Watson Statistic." In The New Palgrave Dictionary of Economics, 3109–11. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2200.

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Hassler, Uwe, and Mehdi Hosseinkouchack. "Distribution of the Durbin–Watson Statistic in Near Integrated Processes." In Empirical Economic and Financial Research, 421–36. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03122-4_26.

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Krämer, Walter. "Durbin–Watson Test." In International Encyclopedia of Statistical Science, 408–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_219.

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King, Maxwell L. "Introduction to Durbin and Watson (1950, 1951) Testing for Serial Correlation in Least Squares Regression. I, II." In Springer Series in Statistics, 229–36. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-4380-9_19.

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Nagar, A. L., and P. D. Sharma. "An Asymptotic Approximation to the Probability Density Function of the Durbin Watson Test Statistic." In Economics, Econometrics and the LINK: Essays in Honor of Lawrence R.Klein, 75–86. Emerald Group Publishing Limited, 1995. http://dx.doi.org/10.1108/s0573-8555(1995)0000226009.

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Blattberg, Robert C. "EVALUATION OF THE POWER OF THE DURBIN-WATSON STATISTIC FOR NON-FIRST ORDER SERIAL CORRELATION ALTERNATIVES." In Perspectives on Promotion and Database Marketing, 17–24. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814287067_0002.

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Mark, Howard, and Jerry Workman. "Linearity in Calibration, Act III Scene II: A Discussion of the Durbin-Watson Statistic, a Step in the Right Direction ☆." In Chemometrics in Spectroscopy, 433–40. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-805309-6.00065-9.

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Mark, Howard, and Jerry Workman. "Linearity in Calibration: Act III Scene II – A Discussion of the Durbin-Watson Statistic, a Step in the Right Direction." In Chemometrics in Spectroscopy, 427–34. Elsevier, 2007. http://dx.doi.org/10.1016/b978-012374024-3/50065-9.

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"Serial correlation and Durbin–Watson bounds." In Past, Present, and Future of Statistical Science, 327–32. Chapman and Hall/CRC, 2014. http://dx.doi.org/10.1201/b16720-34.

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