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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국전산응용수학회 Journal of applied mathematics & informatics Journal of applied mathematics & informatics 제27권 제1호
- 발행연도
- 2009.1
- 수록면
- 315 - 326 (12page)
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초록· 키워드
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The unknown parameters in regression models are usually estimated by using various existing methods. There are several existing methods, such as the least squares method, which is the most common one, the least absolute deviation method, the regression quantile method, and the asymmetric least squares method. For the nonlinear time series regression models, which do not satisfy the general conditions, we will compare them in two ways: 1) a theoretical comparison in the asymptotic sense and 2) an empirical comparison using Monte Carlo simulation for a small sample size.
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참고문헌 (11)
참고문헌 신청
Debasis Kundu / 1993 / Asymptotic theory of least squares estimator of a particular nonlinear regression model / Statistics & Probability Letters 18:13~17
Debasis Kundu / 1996 / Asymptotic theory of least squares estimator of a nonlinear time series regression model / Commun. Statist.-Theory Math 23(1):133~141
Hannan, E. J / 1973 / The estimation of Frequency / Journal of Applied Probability 10:510~519
Jennrich, R / 1969 / Asymptotic properties of nonlinear least squares estimations / Annals of Math-ematical Statistics 40:633~643
Koenker, R / 1978 / Asymptotic theory of least absolute error regression / Journal of the American Statistical Association 73:618~621
Debasis Kundu / 1996 / Asymptotic theory of least squares estimator of a nonlinear time series regression model / Commun. Statist.-Theory Math 23(1):133~141
Hannan, E. J / 1973 / The estimation of Frequency / Journal of Applied Probability 10:510~519
Jennrich, R / 1969 / Asymptotic properties of nonlinear least squares estimations / Annals of Math-ematical Statistics 40:633~643
Koenker, R / 1978 / Asymptotic theory of least absolute error regression / Journal of the American Statistical Association 73:618~621
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