In this paper, we derive general formulae for second-order biases of maximum likelihood estimates which can be applied to a wide class of multivariate nonlinear regression models. The class of models we consider is very rich and includes a number of commonly used models in econometrics and statistic
Bias correction in a multivariate normal regression model with general parameterization
β Scribed by Alexandre G. Patriota; Artur J. Lemonte
- Book ID
- 108267641
- Publisher
- Elsevier Science
- Year
- 2009
- Tongue
- English
- Weight
- 598 KB
- Volume
- 79
- Category
- Article
- ISSN
- 0167-7152
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
This paper deals with a bias correction of Akaike's information criterion (AIC) for selecting variables in multivariate normal linear regression models when the true distribution of observation is an unknown non-normal distribution. It is well known that the bias of AIC is O(1), and there are a numb
For multivariate nonlinear regression and multivariate generalized linear regression models, with repeated measurements and possible missing values, we derive the asymptotic normality of a general estimating equations estimator of the regression matrix. We also provide consistent estimators of the c
It is well known that the ordinary least-squares estimates (OLSE) of autoregressive models are biased in small sample. In this paper, an attempt is made to obtain the unbiased estimates in the sense of median or mean. Using Monte Carlo simulation techniques, we extend the median-unbiased estimator p