Modified maximum-likelihood method for non-normal time series revisited

The modified maximum-likelihood method has recently been applied to some non-normal time series models. Our evaluation of these applications revealed that several of the information matrices given in these studies are not correct due to incorrect evaluation of the process mean, and that the estimators for some of the models with a location parameter are not correct. We correct these results. We address to several other issues and propose modifications. We also made some additional simulations, especially for the location parameter case for which there were a very limited number of previous results. Our results indicate that estimations with a location parameter are not as successful as those with no location parameter, and also that the convergence properties of the method are not very favourable.


The limiting distribution of the F-statistic from nonnormal universes
Gueven, Bilgehan (Informa UK Limited, 2006-12-01)
We consider a linear regression model with an unbalanced 1-fold nested error structure, where group effect and error are from nonnormal universes. The limiting distribution of the F-statistic in this model is derived, as the sample size is large and group sizes take values from a finite set of distinct integers. The result is used to approximate the F-distribution quantile and to test the significance of the random effect variance component. Results are also applicable to the F-statistic in the one-way rand...
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Ayhan, Hüseyin Öztaş (Informa UK Limited, 2003-11-01)
The current work deals with modelling of response error components in supervised interview-reinterview surveys. The model considers several stages of an interactive process to obtain and record a response. The response process is evaluated as, controller-interviewer-respondent-interviewer-controller interaction setting under a supervised interviewing process. The allocation of controllers, interviewers and respondents is made by a hierarchical design for the interview-reinterview process. In addition, a cod...
Minimum variance quadratic unbiased estimation for the variance components in simple linear regression with onefold nested error
Gueven, Ilgehan (Informa UK Limited, 2006-01-01)
The explicit forms of the minimum variance quadratic unbiased estimators (MIVQUEs) of the variance components are given for simple linear regression with onefold nested error. The resulting estimators are more efficient as the ratio of the initial variance components estimates increases and are asymptotically efficient as the ratio tends to infinity.
Representation of Multiplicative Seasonal Vector Autoregressive Moving Average Models
Yozgatlıgil, Ceylan (Informa UK Limited, 2009-11-01)
Time series often contain observations of several variables and multivariate time series models are used to represent the relationship between these variables. There are many studies on vector autoregressive moving average (VARMA) models, but the representation of multiplicative seasonal VARMA models has not been seriously studied. In a multiplicative vector model, such as a seasonal VARMA model, the representation is not unique because of the noncommutative property of matrix multiplication. In this articl...
Extended lasso-type MARS (LMARS) model in the description of biological network
Agraz, Melih; Purutçuoğlu Gazi, Vilda (Informa UK Limited, 2019-01-02)
The multivariate adaptive regression splines (MARS) model is one of the well-known, additive non-parametric models that can deal with highly correlated and nonlinear datasets successfully. From our previous analyses, we have seen that lasso-type MARS (LMARS) can be a strong alternative of the Gaussian graphical model (GGM) which is a well-known probabilistic method to describe the steady-state behaviour of the complex biological systems via the lasso regression. In this study, we extend our original LMARS m...
Citation Formats
T. ULA and C. Yozgatlıgil, “Modified maximum-likelihood method for non-normal time series revisited,” COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, pp. 397–417, 2004, Accessed: 00, 2020. [Online]. Available: