CAJ | 학술논문

提出一种新的递推有界广义极大似然类（GM）回归估计器，新估计器所用的风险函数基于更一般的框架，并采用有界 M-估计函数。设计一个新的权函数拒绝或降低异常点对估计结果的影响，并增加一个增广项，提出一种具有较强自适应能力的面向自回归（AR）模型参数估计的算法。仿真结果表明：提出的GM回归估计器及面向AR模型的算法对异常点不利影响（主要来自于回归变量中的加性异常点）的抑制效果均优于其他GM估计器；在参数不做任何调整的情况下，面向AR模型的算法对非平稳环境下的估计具有良好的估计精度和收敛性。
제출일충신적체추유계엄의겁대사연류（GM）회귀고계기，신고계기소용적풍험함수기우경일반적광가，병채용유계 M-고계함수。설계일개신적권함수거절혹강저이상점대고계결과적영향，병증가일개증엄항，제출일충구유교강자괄응능력적면향자회귀（AR）모형삼수고계적산법。방진결과표명：제출적GM회귀고계기급면향AR모형적산법대이상점불리영향（주요래자우회귀변량중적가성이상점）적억제효과균우우기타GM고계기；재삼수불주임하조정적정황하，면향AR모형적산법대비평은배경하적고계구유량호적고계정도화수렴성。
A new recursive bounded GM estimator for regression is proposed.Unlike other GM estimators,the new esti-mator is based on one more general framework and uses a cost function with bounded M-estimate function.The new esti-mator,in effect,is a recursive one-step iteration solution of the"normal equations"corresponding to the cost function.In the new estimator,a weight function is designed to reject or to reduce the influence of the outliers.Furthermore,by in-troducing an augment variable,the proposed estimator is modified to a very adaptive version for the estimation of autore-gressive parameters.The simulation results show that both the proposed estimator and its modification are more effective than other related estimators in suppressing the adverse influence of outliers;the proposed estimator,with the same set-tings,can keep a high accuracy and stable convergence performance in a variety of non-stationary environments.