• Journal of the Korean Statistical Society
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• 제25권3호
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• pp.303-311
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• 1996

We consider the discrete time randomly perturbed systems on sep-arable Banach space given by $X_{n+1};=;{Gamma}_{n+1}(X_n);+;{epsilon}_{n+1}$ where {${Gamma}_n$} is a sequence of random functions and {${epsilon}_n$} is a sequence of disturbances Sufficient conditions for non-ergodicity of {$X_n$} are obtained.

• 대한수학회보
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• 제31권2호
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• pp.289-295
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• 1994

Let (A,G,.alpha.) be a $C^{*}$-dynamical system. In [3] the classical notions of ergodic properties of topological dynamical systems such as topological transitivity, minimality, and uniquely ergodicity are extended and analyzed in the context of non-abelian $C^{*}$-dynamical systems. We showed in [2] that if G is a compact group, then minimality, topological transitivity, uniquely ergodicity, and weakly ergodicity of the $C^{*}$-dynamical system (A,G,.alpha.) are equivalent.alent.

• 대한수학회지
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• 제33권2호
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• pp.227-234
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• 1996

Consider nonlinear autoregressive processes of order 1 defined by the random iteration $$ (1) X_{n + 1} = f(X_n) + \epsilon_{n + 1} (n \geq 0) $$ where f is real-valued Borel measurable functin on $R^1, {\epsilon_n : n \geq 1}$ is an i.i.d.sequence whose common distribution F has a non-zero absolutely continuous component with a positive density, $E$\mid$\epsilon_n$\mid$ < \infty$, and the initial $X_0$ is independent of ${\epsilon_n : n > \geq 1}$.

• Journal of the Korean Statistical Society
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• 제24권1호
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• pp.149-160
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• 1995

We consider the problem of optimal adaptive estiamtion of the euclidean parameter vector $\theta$ of the univariate non-linerar autogressive time series model ${X_t}$ which is defined by the following system of stochastic difference equations ; $X_t = \sum^p_{i=1} \theta_i \cdot T_i(X_{t-1})+e_t, t=1, \cdots, n$, where $\theta$ is the unknown parameter vector which descrives the deterministic dynamics of the stochastic process ${X_t}$ and ${e_t}$ is the sequence of white noises with unknown density $f(\cdot)$. Under some general growth conditions on $T_i(\cdot)$ which guarantee ergodicity of the process, we construct a sequence of adaptive estimatros which is locally asymptotic minimax (LAM) efficient and also attains the least possible covariance matrix among all regular estimators for arbitrary symmetric density.