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广义结构化模型AI 解读

We present a general class of nonlinear regression and time series models that we call generalised structured models. The class is a natural generalisation of generalised additive models, and it includes generalised interaction models, structured volatility models, visual GARCH, generalised autoregressive conditional heteroscedasticity, models and varying coefficient models. We discuss estimation principles including smoothing splines and a generalisation of the projection…

保序非参数回归及其条件分布与分位数函数估计应用AI 解读

In some regression problems we observe a “response” Yti to level t of a “treatment” applied to an individual with level Xi of a given characteristic, where it has been established that response is monotone increasing in the level of the treatment. A related problem arises when estimating conditional distributions, where the raw data are typically independent and identically distributed pairs (Xi, Zi), and Yti denotes the proportion of Zi's that do not exceed t. We expect the…

用于时变谱分析的平滑样条 ANOVAAI 解读

In this article we propose a smoothing spline ANOVA model (SS-ANOVA) to estimate and to make inference on the time-varying log-spectrum of a locally stationary process. The time-varying spectrum is assumed to be smooth in both time and frequency. This assumption essentially turns a time-frequency spectral estimation problem into a 2-dimensional surface estimation problem. A smooth localized complex exponential (SLEX) basis is used to calculate the initial periodograms, and a…

校准自由度：自动数据平滑与有效曲线检验AI 解读

Curve fitting and curve checking based on the local polynomial regression technique are commonly used data-analytic methods in statistics. This article examines, in nonparametric settings, both the asymptotic expressions and empirical formulas for degrees of freedom (DF), a notion introduced by Hastie and Tibshirani, of linear smoothers. The asymptotic results give useful insights into the nonparametric modeling complexity. Meanwhile, by substituting the exact DFs by the…

灵活的多元GARCH建模及其在国际股票市场中的应用AI 解读

This paper offers a new approach to estimating time-varying covariance matrices in the framework of the diagonal-vech version of the multivariate GARCH(1,1) model. Our method is numerically feasible for large-scale problems, produces positive semidefinite conditional covariance matrices, and does not impose unrealistic a priori restrictions. We provide an empirical application in the context of international stock markets, comparing the new estimator with a number of…

分段协整的推断AI 解读

A cointegration relation is often interpreted as a long-run equilibrium relation whereas short-run deviations are allowed to a certain extent. In practice, however, real data often fail to confirm cointegration for a well understood economic relation. This paper proposes that in many cases failure of confirming cointegration is due to nonstationary deviations in a relatively small portion of the data period (“short-run”) whereas a cointegration relation prevails in the other…

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季节性检验AI 解读

stochastic seasonality proposed by Canova and Hansen. A simplified form of the test statistic in which the nonparametric correction for serial correlation is based on estimates of the spectrum at the seasonal frequencies is considered and shown to have the same asymptotic distribution as the original formulation. Under the null hypothesis, the distribution of the seasonality test statistics is not affected by the inclusion of trends, even when modified to allow for…

推断交易中的私人信息含量：状态切换方法AI 解读

Abstract This paper presents an empirical model for inferring the private information content of trades at the transaction level. The trade‐indicator model of Glosten and Harris ( 1988 ) is extended to a two‐state regime‐switching setting, and the model is estimated using tick‐by‐tick data from the New York Stock Exchange (NYSE). The specialist is found to react in accordance with the proposed model. Bid–ask quotes set after the execution of a trade reflect the conjectured…

纵向计数数据的序列相关伽马脆弱性模型AI 解读

A Poisson‐gamma model is introduced to account for between‐subjects heterogeneity and within‐subjects serial correlation occurring in longitudinal count data. The model extends the usual time‐constant shared frailty approach to allow time‐varying serially correlated gamma frailty whilst retaining standard marginal assumptions. A composite likelihood approach to estimation and testing for serial correlation is proposed. The work is motivated by a clinical trial on…

隐 Markov 模型中的 SPRT 与 CUSUMAI 解读

In this paper, we study the problems of sequential probability ratio tests for parameterized hidden Markov models. We investigate in some detail the performance of the tests and derive corrected Brownian approximations for error probabilities and expected sample sizes. Asymptotic optimality of the sequential probability ratio test for testing simple hypotheses based on hidden Markov chain data is established. Next, we consider the cumulative sum (CUSUM) procedure for change…

马尔可夫体制转换下单位根检验的渐近性质AI 解读

processes observationally equivalent and that unit root tests virtually have no power to detect stationary processes around switching trends, although autocorrelation-robust unit root tests are not affected by size distortions. Conversely, Markov switches in the mean of the transitory components do not change the usual asymptotic properties of the tests. Finally, it is shown that in large samples Markov-switching variances cause neither size distortions nor inconsistency of…

模型设定错误下断点估计量的一般一致性AI 解读

This paper considers the asymptotic behavior of the break-point estimator when some or all of the variables in a structural-break model are misspecified. An obvious example is misspecifying a linear model as a log--log model. The results given here cover a large number of data transformations, including transformation of the independent variables, dependent variable and transformation of both variables at the same time. The true data generating process can be stationary or…

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核苷酸序列沿线系统发育变异的推断AI 解读

We develop a Bayesian multiple changepoint model to infer spatial phylogenetic variation (SPV) along aligned molecular sequence data. SPV occurs in sequences from organisms that have undergone biological recombination or when evolutionary rates and selective pressures vary along the sequences. This Bayesian approach permits estimation of uncertainty regarding recombination, the crossing-over locations, and all other model parameters. The model assumes that the sites along…

简单线性回归的序贯监测AI 解读

This article considers an important aspect of the general sequential analysis problem where a process is in control up to some unknown point i = ν − 1, after which the distribution from which the observations are generated changes. An extensive sequential analytic literature assumes that the change in distribution is abrupt, for example, from N(0,1) to N(μ,1). There is also an extensive literature that deals with a gradual change in the case where the decision (whether or…

Cox 回归中的迭代残差与时变协变量效应AI 解读

Summary The Cox proportional hazards model, which is widely used for the analysis of treatment and prognostic effects with censored survival data, makes the assumption that the hazard ratio is constant over time. Nonparametric estimators have been developed for an extended model in which the hazard ratio is allowed to change over time. Estimators based on residuals are appealing as they are easy to use and relate in a simple way to the more restricted Cox model estimator.…

加性与乘性风险率函数的核估计AI 解读

We propose new procedures for estimating the component functions in both additive and multiplicative nonparametric marker-dependent hazard models. We work with a full counting process framework that allows for left truncation and right censoring and time-varying covariates. Our procedures are based on kernel hazard estimation as developed by Nielsen and Linton and on the idea of marginal integration. We provide a central limit theorem for the marginal integration estimator.…

具有未知协变量阈值变点的 Cox 回归估计AI 解读

We consider a nonregular Cox model for independent and identically distributed right censored survival times, with a change-point according to the unknown threshold of a covariate. The maximum partial likelihood estimators of the parameters and the estimator of the baseline cumulative hazard are studied. We prove that the estimator of the change-point is n-consistent and the estimator of the regression parameters are $n^{1/2}$-consistent, and we establish the asymptotic…

自适应变系数线性模型AI 解读

Summary Varying-coefficient linear models arise from multivariate nonparametric regression, non-linear time series modelling and forecasting, functional data analysis, longitudinal data analysis and others. It has been a common practice to assume that the varying coefficients are functions of a given variable, which is often called an index. To enlarge the modelling capacity substantially, this paper explores a class of varying-coefficient linear models in which the index is…

间接推断、干扰参数与门限移动平均模型AI 解读

nuisance parameter is not identified under the null hypothesis. We develop a testing procedure adapted to this simulation-based estimation method, and detail its use for detecting the threshold effect in threshold moving average models with contemporaneous and lagged asymmetries. In contrast to existing threshold models, these models allow taking into account the presence of asymmetric effects of current and lagged random shocks. We use them to measure the persistence of…

时变平滑转换自回归模型AI 解读

perceived as competing alternatives to linearity. In this article we study the so-called time-varying smooth transition autoregressive (TV-STAR) model, which can be used both for describing simultaneous nonlinearity and structural change and for distinguishing between these features. Two modeling strategies for empirical specification of TV-STAR models are developed. Monte Carlo simulations show that neither of the two strategies dominates the other. A…

内生延迟门限模型的贝叶斯分析AI 解读

autoregressive models: endogenous delay threshold. We apply our methods to the commonly used sunspot data set and find strong evidence in favor of the Endogenous Delay Threshold Autoregressive (EDTAR) model over linear and traditional threshold autoregressions.