- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 862398, 19 pages
Hypothesis Testing in Generalized Linear Models with Functional Coefficient Autoregressive Processes
1School of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, China
2Department of Mathematics, Huizhou University, Huizhou 516007, China
Received 28 January 2012; Accepted 25 March 2012
Academic Editor: Ming Li
Copyright © 2012 Lei Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The paper studies the hypothesis testing in generalized linear models with functional coefficient autoregressive (FCA) processes. The quasi-maximum likelihood (QML) estimators are given, which extend those estimators of Hu (2010) and Maller (2003). Asymptotic chi-squares distributions of pseudo likelihood ratio (LR) statistics are investigated.
Consider the following generalized linear model: where is -dimensional unknown parameter, are functional coefficient autoregressive processes given by where are independent and identically distributed random variable errors with zero mean and finite variance , is a one-dimensional unknown parameter, and is a real valued function defined on a compact set which contains the true value as an inner point and is a subset of . The values of and are unknown. is a known continuous differentiable function.
Model (1.1) includes many special cases, such as an ordinary regression model (when; see [1–7]), an ordinary generalized regression model (when ; see [8–13]), a linear regression model with constant coefficient autoregressive processes (when , ; see [14–16]), time-dependent and function coefficient autoregressive processes (when ; see ), constant coefficient autoregressive processes (when , ; see [18–20]), time-dependent or time-varying autoregressive processes (when ; see [21–23]), and a linear regression model with functional coefficient autoregressive processes (when; see ). Many authors have discussed some special cases of models (1.1) and (1.2) (see [1–24]). However, few people investigate the model (1.1) with (1.2). This paper studies the model (1.1) with (1.2). The organization of this paper is as follows. In Section 2, some estimators are given by the quasi-maximum likelihood method. In Section 3, the main results are investigated. The proofs of the main results are presented in Section 4, with the conclusions and some open problems in Section 5.
2. The Quasi-Maximum Likelihood Estimate
Write the “true” model as where . Define, and by (2.2), we have Thus is measurable with respect to the field generated by, and
Assume at first that the are i.i.d. , we get the log-likelihood of conditional on given by At this stage we drop the normality assumption, but still maximize (2.5) to obtain QML estimators, denoted by. The estimating equations for unknown parameters in (2.5) may be written as Thus, satisfy the following estimation equations where
Remark 2.1. If , then the above equations become the same as Hu’s (see ). If ,, then the above equations become the same as Maller’s (see ). Thus we extend those QML estimators of Hu  and Maller .
For ease of exposition, we will introduce the following notations, which will be used later in the paper. Let vector . Define By (2.7), we have where the * indicates that the elements are filled in by symmetry, Because and are mutually independent, we have where By (2.8) (2.7) and, we have
3. Statement of Main Results
In the section pseudo likelihood ratio (LR) statistics for various hypothesis tests of interest are derived. We consider the following hypothesis: When the parameter space is restricted by a hypothesis , letbe the corresponding QML estimators of , and let be minus twice the log-likelihood, evaluated at the fitted parameters. Also let be the “deviance” statistic for testing against. From (2.5) and (2.8), and similarly
In order to obtain our results, we give some sufficient conditions as follows.(A1) is positive definite for sufficiently large and where and denotes the maximum in absolute value of the eigenvalues of a symmetric matrix.(A2) There is a constant such that(A3)andexist and are bounded, andis twice continuously differentiable, , .
4. Proof of Theorem
To prove Theorem 3.1, we first introduce the following lemmas.
Lemma 4.1. Suppose that (A1)–(A3) hold. Then, for all , where
Lemma 4.2. Suppose that (A1)–(A3) hold. Then , and where are on the line ofand.
Proof of Theorem 3.1. Note that and are nonsingular. By Taylor’s expansion, we have
where for some . Since , also . By (4.1), we have
Thus is a symmetric matrix with. By (4.5) and (4.6), we have
Letdenoteand, respectively. By (4.7), we have
By (2.15), (4.2) and (4.8), we get
By (2.1), (2.11) and (4.12), we have
By (4.13) and (2.10), we have
By (4.13), we have
By (4.15), we have
By (4.14) and (4.16), we have
By (4.15), we have
Thus, by (4.17) and (4.18), we have
Since , we have
Thus, by (4.17), (4.20) and mean value theorem, we have
where for some .
It is easy to know that By Lemma 4.2 and (4.22), we have Hence, by (4.11), we have By (4.24), we have By Lemma 4.2, we have Now, we prove (3.8). By (4.12), we have Note that From (4.28), we have By (2.8) and (2.10), we have From (4.30), we obtain that By (4.29), (4.31) and Lemma 4.2, we have By (3.3)–(3.5), we have Under the , and by (4.26), (4.32) and (4.33), we have It is easily proven that Thus, by (4.33)–(4.35), we finish the proof of (3.8).
Next we prove (3.9). Under, , and , we have Hence By (2.8), (2.10), we have From (4.38), we obtain, Thus, by (4.37), (4.39) and Lemma 4.2, we have By (3.3)–(3.5), we have Under the, by (4.26), (4.40), and (4.41), we obtain Thus, by (4.35), (4.42), (3.9) holds.
Finally, we prove (3.10). Under, we have Thus By (2.8) and (2.10), we have From (4.45), we obtain By (4.44), (4.46) and Lemma 4.2, we have By (3.3)–(3.5), we know that Under the , by (4.26), (4.47) and (4.48), we have Thus, (3.10) follows from (4.48), (4.49), and (4.35). Therefore, we complete the proof of Theorem 3.1.
5. Conclusions and Open Problems
In the paper, we consider the generalized linear mode with FCA processes, which includes many special cases, such as an ordinary regression model, an ordinary generalized regression model, a linear regression model with constant coefficient autoregressive processes, time-dependent and function coefficient autoregressive processes, constant coefficient autoregressive processes, time-dependent or time-varying autoregressive processes, and a linear regression model with functional coefficient autoregressive processes. And then we obtain the QML estimators for some unknown parameters in the generalized linear mode model and extend some estimators. At last, we use pseudo LR method to investigate three hypothesis tests of interest and obtain the asymptotic chi-squares distributions of statistics.
However, several lines of future work remain open.
(1) It is well known that a conventional time series can be regarded as the solution to a differential equation of integer order with the excitation of white noise in mathematics, and a fractal time series can be regarded as the solution to a differential equation of fractional order with a white noise in the domain of stochastic processes (see ). In the paper, is a conventional nonlinear time series. We may investigate some hypothesis tests by pseudo LR method when theis a fractal time series (the idea is given by an anonymous reviewer). In particular, we assume that where is strictly decreasing sequence of nonnegative numbers, is a constant sequence, and is the Riemann-Liouville integral operator of order given by where is the Gamma function, and is a piecewise continuous on and integrable on any finite subinterval of (See [25, 26]). Fractal time series may have a heavy-tailed probability distribution function and has been applied various fields of sciences and technologies (see [25, 27–32]). Thus it is very significant to investigate various regression models with fractal time series errors, including regression model (1.1) with (5.1).
(2) We maybe investigate the others hypothesis tests, for example::; :; :; : and is a continuous function,;:, ;:, .
The authors would like to thank the anonymous referees for their valuable comments which have led to this much improved version of the paper. The paper was supported by Scientific Research Item of Department of Education, Hubei (no. D20112503), Scientific Research Item of Ministry of Education, China (no. 209078), and Natural Science Foundation of China (no. 11071022, 11101174).
- Z. D. Bai and M. Guo, “A paradox in least-squares estimation of linear regression models,” Statistics & Probability Letters, vol. 42, no. 2, pp. 167–174, 1999.
- Y. Li and H. Yang, “A new stochastic mixed ridge estimator in linear regression model,” Statistical Papers, vol. 51, no. 2, pp. 315–323, 2010.
- A. E. Hoerl and R. W. Kennard, “Ridge regression: Biased estimation for nonorthogonal problems,” Technometrics, vol. 12, pp. 55–67, 1970.
- X. Chen, “Consistency of LS estimates of multiple regression under a lower order moment condition,” Science in China Series A, vol. 38, no. 12, pp. 1420–1431, 1995.
- T. W. Anderson and J. B. Taylor, “Strong consistency of least squares estimates in normal linear regression,” The Annals of Statistics, vol. 4, no. 4, pp. 788–790, 1976.
- G. González-Rodríguez, A. Blanco, N. Corral, and A. Colubi, “Least squares estimation of linear regression models for convex compact random sets,” Advances in Data Analysis and Classification, vol. 1, no. 1, pp. 67–81, 2007.
- H. Cui, “On asymptotics of t-type regression estimation in multiple linear model,” Science in China Series A, vol. 47, no. 4, pp. 628–639, 2004.
- M. Q. Wang, L. X. Song, and X. G. Wang, “Bridge estimation for generalized linear models with a diverging number of parameters,” Statistics & Probability Letters, vol. 80, no. 21-22, pp. 1584–1596, 2010.
- L. C. Chien and T. S. Tsou, “Deletion diagnostics for generalized linear models using the adjusted Poisson likelihood function,” Journal of Statistical Planning and Inference, vol. 141, no. 6, pp. 2044–2054, 2011.
- L. Fahrmeir and H. Kaufmann, “Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models,” The Annals of Statistics, vol. 13, no. 1, pp. 342–368, 1985.
- Z. H. Xiao and L. Q. Liu, “Laws of iterated logarithm for quasi-maximum likelihood estimator in generalized linear model,” Journal of Statistical Planning and Inference, vol. 138, no. 3, pp. 611–617, 2008.
- Y. Bai, W. K. Fung, and Z. Zhu, “Weighted empirical likelihood for generalized linear models with longitudinal data,” Journal of Statistical Planning and Inference, vol. 140, no. 11, pp. 3446–3456, 2010.
- S. G. Zhao and Y. Liao, “The weak consistency of maximum likelihood estimators in generalized linear models,” Science in China A, vol. 37, no. 11, pp. 1368–1376, 2007.
- P. Pere, “Adjusted estimates and Wald statistics for the AT(1) model with constant,” Journal of Econometrics, vol. 98, no. 2, pp. 335–363, 2000.
- R. A. Maller, “Asymptotics of regressions with stationary and nonstationary residuals,” Stochastic Processes and Their Applications, vol. 105, no. 1, pp. 33–67, 2003.
- W. A. Fuller, Introduction to Statistical Time Series, John Wiley & Sons, New York, NY, USA, 2nd edition, 1996.
- G. H. Kwoun and Y. Yajima, “On an autoregressive model with time-dependent coefficients,” Annals of the Institute of Statistical Mathematics, vol. 38, no. 2, pp. 297–309, 1986.
- J. S. White, “The limiting distribution of the serial correlation coefficient in the explosive case,” Annals of Mathematical Statistics, vol. 29, pp. 1188–1197, 1958.
- J. S. White, “The limiting distribution of the serial correlation coefficient in the explosive case—II,” Annals of Mathematical Statistics, vol. 30, pp. 831–834, 1959.
- J. D. Hamilton, Time Series Analysis, Princeton University Press, Princeton, NJ, USA, 1994.
- F. Carsoule and P. H. Franses, “A note on monitoring time-varying parameters in an autoregression,” International Journal for Theoretical and Applied Statistics, vol. 57, no. 1, pp. 51–62, 2003.
- R. Azrak and G. Mélard, “Asymptotic properties of quasi-maximum likelihood estimators for ARMA models with time-dependent coefficients,” Statistical Inference for Stochastic Processes, vol. 9, no. 3, pp. 279–330, 2006.
- R. Dahlhaus, “Fitting time series models to nonstationary processes,” The Annals of Statistics, vol. 25, no. 1, pp. 1–37, 1997.
- H. Hu, “QML estimators in linear regression models with functional coefficient autoregressive processes,” Mathematical Problems in Engineering, vol. 2010, Article ID 956907, 30 pages, 2010.
- M. Li, “Fractal time series—a tutorial review,” Mathematical Problems in Engineering, vol. 2010, Article ID 157264, 26 pages, 2010.
- Y. S. Mishura, Stochastic Calculus for Fractional Brownian Motion and Related Processes, vol. 1929 of Lecture Notes in Mathematics, Springer, Berlin, Germany, 2008.
- M. Li and W. Zhao, “Visiting power laws in cyber-physical networking systems,” Mathematical Problems in Engineering, vol. 2012, Article ID 302786, 13 pages, 2012.
- M. Li, C. Cattani, and S. Y. Chen, “Viewing sea level by a one-dimensional random function with long memory,” Mathematical Problems in Engineering, vol. 2011, Article ID 654284, 13 pages, 2011.
- C. Cattani, “Fractals and hidden symmetries in DNA,” Mathematical Problems in Engineering, vol. 2010, Article ID 507056, 31 pages, 2010.
- J. Lévy-Véhel and E. Lutton, Fractals in Engineering, Springer, 1st edition, 2005.
- V. Pisarenko and M. Rodkin, Heavy-Tailed Distributions in Disaster Analysis, Springer, 2010.
- H. Sheng, Y.-Q. Chen, and T.-S. Qiu, Fractional Processes and Fractional Order Signal Processing, Springer, 2012.