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High‐dimensional non‐stationary time series, which reveal both complex trends and stochastic behaviour, occur in many scientific fields, e.g. macroeconomics, finance, neuroeconomics, etc. To model these, we propose a generalized dynamic semi‐parametric factor model with a two‐step estimation procedure. After choosing smoothed functional principal components as space functions (factor loadings), we extract various temporal trends by employing variable selection techniques for…

关于提升方法论文的讨论及作者回应AI 解读

Discussions of: "Process consistency for AdaBoost" [Ann. Statist. 32 (2004), no. 1, 13-29] by W. Jiang; "On the Bayes-risk consistency of regularized boosting methods" [ibid., 30-55] by G. Lugosi and N. Vayatis; and "Statistical behavior and consistency of classification methods based on convex risk minimization" [ibid., 56-85] by T. Zhang. Includes rejoinders by the authors.

可用于代入估计的非参数估计量AI 解读

We consider nonparametric estimation of an object such as a probability density or a regression function. Can such an estimator achieve the ratewise minimax rate of convergence on suitable function spaces, while, at the same time, when "plugged-in," estimate efficiently (at a rate of~$n^{-1/2}$ with the best constant) many functionals of the object? For example, can we have a density estimator whose definite integrals are efficient estimators of the cumulative distribution…

删失数据的单调估计方程AI 解读

The monotone class rank-test-based estimating equations for regression models with right censored data is considered. We introduce an estimator which is a solution of a monotone estimating equation that is an extension of the Gehan test. The estimator is easy to derive, $\sqrt n$-consistent and asymptotically normal under minimal conditions. All monotone estimating equations are characterized, and a simulation study, which shows that our suggested procedure performs well, is…

具有误报概率界的动态抽样正态均值变化检测AI 解读

We show that when dynamic sampling is feasible, there exist surveillance schemes for which the probability of a false alarm is bounded and which have a bounded expected delay when detecting a (true) change. In the case of detecting a change of a normal mean, we probe optimality and suggest procedures. These procedures compare favorably to those having a fixed sampling rate which have been developed for an expectation constraint on the average run length until a false alarm.

有序列联表的序约束 RC 模型：估计与拟合检验AI 解读

The RC model has been proposed as a model for ordered contingency tables. It involves parametric scores that are assigned to the rows and columns of the table so that these scores reflect the ordinality of the row and column categories. Efficient estimation of these parameters subject to order constraints remained an open problem mainly due to severe difficulties in computing these estimates and difficulties in deriving an appropriate asymptotic goodness-of-fit test. A…

已知边际分布时概率测度线性泛函的有效估计AI 解读

Suppose that $P$ is the distribution of a pair of random variables $(X, Y)$ on a product space $\mathbb{X} \times \mathbb{Y}$ with known marginal distributions $P_X$ and $P_Y$. We study efficient estimation of functions $\theta(h) = \int h dP$ for fixed $h: \mathbb{X} \times \mathbb{Y} \rightarrow R$ under iid sampling of $(X, Y)$ pairs from $P$ and a regularity condition on $P$. Our proposed estimator is based on partitions of both $\mathbb{X}$ and $\mathbb{Y}$ and the…

We consider dynamic procedures for sampling from a process (a Brownian motion) and stopping it after a change is detected. The basic idea is to conduct a sequence of similar SPRT's, each one of them done in negligible time, while not sampling at all between them. The procedures detect the change point much faster than the standard procedures with the same sampling rate and time to false alarm, but hold the sampling rate constant.

带随机干扰参数的信号估计AI 解读

We describe two models in which n records of a signal in white noise are taken. In the first model the signal parameters of interest do not change between records, but the amplitude varies in a random way. In the second model, the location is the random nuisance parameter. We describe an efficient estimator for the first model. For the second model an estimator is described which is n½ consistent, invariant to the distribution of the location and efficient for a particular…

带随机干扰参数的信号估计AI 解读

We describe two models in which n records of a signal in white noise are taken. In the first model the signal parameters of interest do not change between records, but the amplitude varies in a random way. In the second model, the location is the random nuisance parameter. We describe an efficient estimator for the first model. For the second model an estimator is described which is n½ consistent, invariant to the distribution of the location and efficient for a particular…

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稳健贝叶斯决策程序：数据分布中的严重误差AI 解读

We consider a standard Bayes decision situation except that with small probability the data may be irrelevant to the parameter of interest (i.e. the experiment is "biased"). The minimax solution under quite general assumptions is described and discussed.