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论预测最小二乘准则AI 解读

Recently, Rissanen proposed a new model selection criterion PLS that selects the model that minimizes the accumulated squares of prediction errors. Usually, the information-based criteria, such as AIC and BIC, select the model that minimizes a loss function which can be expressed as a sum of two terms. One measures the goodness of fit and the other penalizes the complexity of the selected model. In this paper we provide such an interpretation for PLS. Using this…

带移民分枝过程的均值估计AI 解读

Let $\{X_n\}$ be the branching process with immigration and let $m$ and $\lambda$ be the means of the offspring and immigration distributions, respectively. Estimation results for $m$ and $\lambda$ were obtained in the literature for the subcritical $(m < 1)$ and supercritical $(m > 1)$ cases, but no unified estimation procedure was developed, which would allow inference without knowledge of the range of $m$. The goal of this paper is to investigate this problem.

不可逆自适应分配规则AI 解读

Motivated by a scheduling problem arising from serial sacrifice experiments, the asymptotic efficiency of irreversible adaptive allocation rules is studied. The asymptotic lower bound for the regret of an adaptive allocation rule is characterized by the minimum of a linear program. Based on a class of one-sided sequential tests, asymptotically efficient rules which achieve the lower bound are constructed. The conditions necessary for this construction are verified in the…

不稳定自回归过程最小二乘估计的极限分布AI 解读

An autoregressive process $y_n = \beta_1y_{n-1} + \cdots + \beta_py_{n-p} + \varepsilon_n$ is said to be unstable if the characteristic polynomial $\phi(z) = 1 - \beta_1z - \cdots - \beta_pz^p$ has all roots on or outside the unit circle. The limiting distribution of the least squares estimate of $(\beta_1, \cdots, \beta_p)$ is derived and characterized as a functional of stochastic integrals under a $2 + \delta$ moment assumption on $\varepsilon_n$. Up to the present,…

随机回归模型中最小二乘预测量的自适应预测及时间序列应用AI 解读

Herein we consider the asymptotic performance of the least squares predictors $\hat{y}_n$ of the stochastic regression model $y_n = \beta_1 x_{n1} + \cdots + \beta_p x_{np} + \varepsilon_n$. In particular, the accumulated cost function $\sum^n_{k=1} (y_k - \hat{y}_k - \varepsilon_k)^2$ is studied. The results are then applied to nonstationary autoregressive time series. A statistic is also constructed to show how many times one should difference a nonstationary time series…

近非平稳一阶自回归过程的渐近推断AI 解读

A first-order autoregressive process, $Y_t = \beta Y_{t - 1} + \epsilon_t$, is said to be nearly nonstationary when $\beta$ is close to one. The limiting distribution of the least-squares estimate $b_n$ for $\beta$ is studied when $Y_t$ is nearly nonstationary. By reparameterizing $\beta$ to be $1 - \gamma/n, \gamma$ being a fixed constant, it is shown that the limiting distribution of $\tau_n = (\sum^n_{t = 1}Y^2_{t - 1})^{1/2}(b_n - \beta)$ converges to $\mathscr{L}(\gamma)$…

多元自适应随机逼近AI 解读

Herein we study a multivariate version of the adaptive stochastic approximation developed recently by Lai and Robbins. An adaptive procedure which involves a Venter-type estimate of the Jacobian of the response function is proposed and shown to be asymptotically efficient from both the estimation and the control points of view.

随机回归模型中最小二乘估计的渐近性质AI 解读

Strong consistency of least-squares estimates in stochastic regression models is established under the assumption that the underlying model can be reparametrized so that the new design vectors are weakly correlated. An application to fixed-width interval estimation in stochastic approximation schemes is also discussed.