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逐一估计多个结构断点AI 解读

Sequential (one-by-one) rather than simultaneous estimation of multiple breaks is investigated in this paper. The advantage of this method lies in its computational savings and its robustness to misspecification in the number of breaks. The number of least-squares regressions required to compute all of the break points is of order T , the sample size. Each estimated break point is shown to be consistent for one of the true ones despite underspecification of the number of…

时间序列模型中的自适应估计AI 解读

In a framework particularly suited for many time-series models we obtain a LAN result under quite natural and economical conditions. This enables us to construct adaptive estimators for (part of) the Euclidean parameter in these semiparametric models. Special attention is directed to group models in time series with the important subclass of models with time varying location and scale. Our set-up is confronted with the existing literature and, as examples, we reconsider…

适用于预测的二元脆弱性模型：生物标志物预后应用AI 解读

Serial biological markers are often used in medicine as prognostic indicators. For example, elevated tumour antigen tests presage tumour recurrence; rapid decrease in the CD4 cell counts of HIV + individuals indicate high risk for developing AIDS. Methods for evaluating the usefulness of markers should address how the prediction of future risk is revised, in a way that assists the making of decisions at the time such an indicator is observed. Proportional hazards modelling…

含变点的线性变量误差模型推断AI 解读

A new linear structural errors-in-variables regression with changepoint model is considered. In this model we consider the likelihood ratio test based on the maximum Hotelling T 2 for the test of no change against the alternative of exactly one change. If there is a change, either known a priori or by testing, then we estimate the unknown changepoint parameter and some other related parameters by maximum likelihood. The limiting distribution of the changepoint estimator is…

简化单调回归方法AI 解读

Medical researchers often desire to categorize patients into monotonic response groups based on the relationship between continuous variables. Isotonic regression fits consist of level sets of increasing value, for which the estimated response is constant. However, the number of level sets obtained is often large, preventing simple description. This article introduces two new nonparametric methods called reduced isotonic regression and reduced monotonic regression, the…

摘要暂缺

简化单调回归方法AI 解读

Medical researchers often desire to categorize patients into monotonic response groups based on the relationship between continuous variables. Isotonic regression fits consist of level sets of increasing value, for which the estimated response is constant. However, the number of level sets obtained is often large, preventing simple description. This article introduces two new nonparametric methods called reduced isotonic regression and reduced monotonic regression, the…

摘要暂缺

协整与体制变化：日本消费函数AI 解读

In this paper we examine a model of cointegration where long-run parameters are subject to switching between several different cointegrating regimes. These shifts are allowed to be governed by the outcome of an unobserved Markov chain with unknown transition probabilities. We illustrate this approach using Japanese data on consumption and disposable income, and find that the data favor a Markov-switching long-run relationship over a standard temporally stable formulation.

协整与状态变化：日本消费函数AI 解读

In this paper we examine a model of cointegration where long-run parameters are subject to switching between several different cointegrating regimes. These shifts are allowed to be governed by the outcome of an unobserved Markov chain with unknown transition probabilities. We illustrate this approach using Japanese data on consumption and disposable income, and find that the data favor a Markov-switching long-run relationship over a standard temporally stable formulation.

非平稳过程的时间序列模型拟合AI 解读

A general minimum distance estimation procedure is presented for nonstationary time series models that have an evolutionary spectral representation. The asymptotic properties of the estimate are derived under the assumption of possible model misspecification. For autoregressive processes with time varying coefficients, the estimate is compared to the least squares estimate. Furthermore, the behavior of estimates is explained when a stationary model is fitted to a…

回归残差的迭代部分和与连续性约束下的变点检验AI 解读

Summary Iterated partial sum sequences of regression least squares residuals are defined and large sample properties of sequences of stochastic processes defined by these iterated partial sums are discussed. Also, finite sample properties of the iterated partial sum sequences are obtained. These include a property of least squares residuals of polynomial fits to equispaced data, namely the iterated partial sums sum to 0 provided that the order of iteration is not greater…

季节单位根与季节均值变动的贝叶斯分析AI 解读

In this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observed time series. Seasonal unit root processes are useful to describe economic series with changing seasonal fluctuations. A natural alternative model for similar purposes contains deterministic seasonal mean shifts instead of seasonal stochastic trends. This leads to analysing seasonal unit roots in the presence of mean shifts using Bayesian techniques. Our method is illustrated using…

结构变化检验的近似渐近 p 值AI 解读

Numerical approximations to the asymptotic distributions of recently proposed tests for structural change are presented. This enables easy yet accurate calculation of asymptotic p-values. Keywords: parameter stability, changepoint models

结构变化检验的近似渐近p值AI 解读

Numerical approximations to the asymptotic distributions of recently proposed tests for structural change are presented. This enables easy yet accurate calculation of asymptotic p values. A GAUSS program is available to perform the computations.

离散持续期与竞争风险模型中风险率及时变效应的平滑估计AI 解读

State-space or dynamic approaches to discrete or grouped duration data with competing risks or multiple terminating events allow simultaneous modeling and smooth estimation of hazard functions and time-varying effects in a flexible way. Full Bayesian or posterior mean estimation, using numerical integration techniques or Monte Carlo methods, can become computationally rather demanding or even infeasible for higher dimensions and larger datasets. Therefore, based on previous…

用混合转移分布模型描述时间序列的平坦段、突发与离群值AI 解读

The class of mixture transition distribution (MTD) time series models is extended to general non-Gaussian time series. In these models the conditional distribution of the current observation given the past is a mixture of conditional distributions given each one of the last p observations. They can capture non-Gaussian and nonlinear features such as flat stretches, bursts of activity, outliers changepoints in a single unified model class. They can also represent time series…

离散持续期与竞争风险模型中风险函数和时变效应的平滑AI 解读

State-space or dynamic approaches to discrete or grouped duration data with competing risks or multiple terminating events allow simultaneous modeling and smooth estimation of hazard functions and time-varying effects in a flexible way. Full Bayesian or posterior mean estimation, using numerical integration techniques or Monte Carlo methods, can become computationally rather demanding or even infeasible for higher dimensions and larger datasets. Therefore, based on previous…

标准化 U 统计量最大值的极限定理及其应用AI 解读

We show that the maximally selected standardized U-statistic goes in distribution to an infinite sum of weighted chi-square random variables in the degenerate case. The result is applied to the detection of possible changes in the distribution of a sequence observation.

满足误报概率界的分布变化检测动态抽样策略AI 解读

We show that if dynamic sampling is feasible, then there exist surveillance schemes that satisfy a probability constraint on false alarm. Procedures are suggested for detecting a change of a normal mean from 0 to a (unknown) positive value. These procedures are optimal (up to a constant term) when the post-change mean is known, and almost optimal [up to an $o(\log(1/\alpha))$ term when the post-change mean is unknown.

经济时间序列能通过差分变得平稳吗？AI 解读

This paper considers a class of nonstationary varying coefficient autoregressive models which allow stochastic variability in the autoregressive root. It is argued that such models provide a better description of the behaviour of macroeconomic variables than fixed unit root autoregressive models as they allow more general forms of nonstationarity. We construct a test of the null hypothesis of a fixed unit root against the alternative of a fixed unit root against the…

时变系数混合比例风险模型的可识别性AI 解读

This paper establishes conditions for the nonparametric identifiability of the mixed proportional hazards model with time-varying coefficients. Unlike the mixed proportional hazards model, a regressor with two distinct values is not sufficient to identify this model. An unbounded regressor, however, is sufficient for identification.

长记忆参数变化的检验AI 解读

Long-range dependence is often observed in long time series. Correlations decay approximately like |k|2H-2, with H ε (0.5, 1), as the lag k tends to infinity. The long-term features of the data are essentially characterised by the parameter H. Small changes of H have strong implications for the long-term behaviour of the process. In particular, rates of convergence of estimators for the mean, and for many other parameters of interest, differ for different values of H. For…

条件高斯状态空间模型中的马尔可夫链蒙特卡洛AI 解读

A Bayesian analysis is given for a state space model with errors that are finite mixtures of normals and with coefficients that can assume a finite number of different values. A sequence of indicator variables determines which components the errors belong to and the values of the coefficients. The computation is carried out using Markov chain Monte Carlo, with the indicator variables generated without conditioning on the states. Previous approaches use the Gibbs sampler to…