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Power Divergence Family of Tests for Categorical Time Series Models

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Abstract

A fundamental issue that arises after fitting a regression model is that of testing the goodness of the fit. Our work brings together the power divergence family of goodness of fit tests and regression models for categorical time series. We show that under some reasonable assumptions, the asymptotic distribution of the power divergence family of goodness of fit tests converges to a normal random variable. This fact introduces a novel method for carrying out goodness of fit tests about a regression model for categorical time series. We couple the theory with some empirical results.

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References

  • Agresti, A. (1990). Categorical Data Analysis, Wiley, New York.

    Google Scholar 

  • Andersen, P. K. and Gill, R. D. (1982). Cox's regression models for counting process: A large sample approach, Ann. Statist., 10, 1100–1120.

    Google Scholar 

  • Arjas, E. and Haara, P. (1987). A logistic regression model for hazard: Asymptotic results, Scand. J. Statist., 14, 1–18.

    Google Scholar 

  • Bonney, E. G. (1987). Logistic regression for dependent binary observations, Biometrics, 43, 951–973.

    Google Scholar 

  • Brillinger, D. R. (1996). An analysis of ordinal-valued time series, Athens Conference on Applied Probability and Time Series Analysis, Vol. II:Time Series Analysis in Memory of E. J. Hannan, Lecture Notes in Statist., No. 115, 73–87, Springer, New York.

    Google Scholar 

  • Cox, D. R. (1975). Partial likelihood, Biometrika, 62, 69–76.

    Google Scholar 

  • Cressie, N. A. C. and Read, T. R. C. (1984). Multinomial goodness-of-fit tests, J. Roy. Statist. Soc. Ser. B, 46, 440–464.

    Google Scholar 

  • Dale, J. R. (1986). Asymptotic normality of goodness-of-fit statistics for sparse product multinomials, J. Roy. Statist. Soc. Ser. B, 48, 48–59.

    Google Scholar 

  • Fahrmeir, L. and Kaufmann, H. (1987). Regression models for nonstationary categorical time series, J. Time Ser. Anal., 8, 147–160.

    Google Scholar 

  • Fahrmeir, L. and Tutz, G. (1994). Multivariate Statistical Modeling Based on Generalized Linear Models, Springer, New York.

    Google Scholar 

  • Fokianos, K. and Kedem, B. (1998). Prediction and classification of non-stationary categorical time series, J. Multivariate Anal., 67, 277–296.

    Google Scholar 

  • Fokianos, K. and Kedem, B. (1999). A stochastic approximation algorithm for the adaptive control of time series following generalized linear models, J. Time Ser. Anal., 20, 289–308.

    Google Scholar 

  • Gleser, L. J. and Moore, D. S. (1985). The effect of positive dependence on chi-square tests for categorical data, J. Roy. Statist. Soc. Ser. B, 47, 459–465.

    Google Scholar 

  • Hall, P. and Heyde, C. C. (1980). Martingale Limit Theory and Its Applications, Academic Press, New York.

    Google Scholar 

  • Holst, L. (1972). Asymptotic normality and efficiency for certain goodness of fit tests, Biometrika, 59, 137–145.

    Google Scholar 

  • Kaufmann, H. (1987). Regression models for nonstationary categorical time series: Asymptotic estimation theory, Ann. Statist., 15, 79–98.

    Google Scholar 

  • Koehler, K. J. (1986). Goodness-of-fit tests for log-linear models in sparse contingency tables, J. Amer. Statist. Assoc., 81, 483–493.

    Google Scholar 

  • Korn, E. L. and Whittemore, A. S. (1979). Methods for analyzing panel studies of acute health effects of air pollution, Biometrics, 35, 795–802.

    Google Scholar 

  • Li, W. K. (1991). Testing model adequacy for some markov regression models for time series, Biometrika, 78, 83–89.

    Google Scholar 

  • Liang, K.-Y. and Zeger, S. L. (1989). A class of logistic regression models for multivariate binary time series, J. Amer. Statist. Assoc., 84, 447–451.

    Google Scholar 

  • McCullagh, P. (1980). Regression models for ordinal data (with discussion), J. Roy. Statist. Soc. Ser. B, 42, 109–142.

    Google Scholar 

  • McCullagh, P. (1986). The conditional distribution of goodness-of-fit statistics for discrete data, J. Amer. Statist. Assoc., 81, 104–107.

    Google Scholar 

  • McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2nd ed., Chapman and Hall, London.

    Google Scholar 

  • Morris, C. (1975). Central limit theorem for multinomial sums, Ann. Statist., 3, 165–188.

    Google Scholar 

  • Muenz, L. R. and Rubinstein, L. (1985). Markov models for covariate dependence of binary sequences, Biometrics, 41, 91–101.

    Google Scholar 

  • Osius, G. (1985). Goodness-of-fit tests for binary data with (possible) small expectations but large degrees of freedom, Supplement to Statist. Decisions, 2, 213–224.

    Google Scholar 

  • Osius, G. and Rojek, D. (1992). Normal goodness-of-fit tests for multinomial models with large degrees of freedom, J. Amer. Statist. Assoc., 87, 1145–1152.

    Google Scholar 

  • Read, T. R. C. and Cressie, N. A. C. (1988). Goodness-of-fit Statistics for Discrete Multivariate Data, Springer, New York.

    Google Scholar 

  • Schoenfeld, D. (1980). Chi-square goodness-of-fit test for the proportional hazards regression model, Biometrika, 67, 145–153.

    Google Scholar 

  • Slud, E. and Kedem, B. (1994). Partial likelihood analysis of logistic regression and autoregression, Statist. Sinica, 4, 89–106.

    Google Scholar 

  • Stern, R. D. and Coe, R. (1984). A model fitting analysis of daily rainfall data, J. Roy. Statist. Soc. Ser. A, 147, 1–34.

    Google Scholar 

  • Weiss, L. (1976). The normal approximation to the multinomial with increasing number of classes, Naval Res. Logist. Quarterly, 23, 139–149.

    Google Scholar 

  • Wong, W. H. (1986). Theory of partial likelihood, Ann. Statist., 14, 88–123.

    Google Scholar 

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Fokianos, K. Power Divergence Family of Tests for Categorical Time Series Models. Annals of the Institute of Statistical Mathematics 54, 543–564 (2002). https://doi.org/10.1023/A:1022459010316

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