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Conditional Heteroscedasticity in Qualitative Response Models of Time Series: A Gibbs-Sampling Approach to the Bank Prime Rate

定性响应时间序列的条件异方差:银行最优惠贷款利率的吉布斯抽样分析

Previous time series applications of qualitative response models have ignored features of the data, such as conditional heteroscedasticity, that are routinely addressed in time series econometrics of financial data. This article addresses this issue by adding Markov-switching heteroscedasticity to a dynamic ordered probit model of discrete changes in the bank prime lending rate and estimating via the Gibbs sampler. The dynamic ordered probit model of Eichengreen, Watson, and…

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OPTIMALITY FOR THE INTEGRATED CONDITIONAL MOMENT TEST

积分条件矩检验的最优性

This paper proposes a version of the integrated conditional moment (ICM) test that is optimal for a class of composite alternatives. The ICM test is built on the fact that a random function based on a correctly specified model should have zero mean, whereas any misspecification in the conditional mean implies a divergent mean for the random function. We derive test statistics that are optimal for each basis element of an orthonormal decomposition of the function space for…

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THE LOCAL ASYMPTOTIC POWER OF CERTAIN TESTS FOR FRACTIONAL INTEGRATION

若干分数单整检验的局部渐近功效

It is possible to construct a test of the null of no fractional integration that has nontrivial asymptotic power against a sequence of alternatives specifying that the series is I ( d ) with d = O ( T −1/2 ), where T is the sample size. In this paper, I show that tests for fractional integration that are based on the partial sum process of the time series have only trivial asymptotic power (i.e., equal to the size) against this sequence of local alternatives. These tests…

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COINTEGRATING REGRESSIONS WITH TIME VARYING COEFFICIENTS

具有时变系数的协整回归

This paper considers cointegrating regressions with time varying coefficients. The coefficients are modeled as smooth functions evolving over time. It is shown that they can be estimated nonparametrically, using suitably modified series estimators. Presented is the efficient method of estimation, which relies on simple prefiltering of the data and preestimation of the model. The test for the adequacy of model specification is also developed. Our model and statistical methods…

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Miscellanea. An extension of Morel-Nagaraj's finite mixture distribution for modelling multinomial clustered data

杂录:用于多项聚类数据的 Morel–Nagaraj 有限混合分布扩展

Morel & Nagaraj (1993) proposed a finite mixture distribution for modelling multinomial extra variation when the extra variation is believed to be caused by clumped multinomial sampling with one clumping. We propose an extension of the model to incorporate possible multiple clumping. We show that the extended finite mixture distribution is a multinomial mixing distribution with different mixing coefficients. We derive maximum likelihood estimating equations and an asymptotic…

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A test for symmetries of multivariate probability distributions

多变量概率分布对称性的检验

A Monte Carlo test for multivariate symmetries is proposed. The Monte Carlo simulations are performed conditionally on a minimal sufficient statistic for the class of distributions with symmetric density. Additionally, a general purpose test statistic based on a distance measure between the probability density function and its symmetrised version is introduced. The Monte Carlo tests for spherical symmetry and multivariate reflection symmetry are studied numerically for this…

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Nonparametric regression in the presence of measurement error

存在测量误差时的非参数回归

In many regression applications the independent variable is measured with error. When this happens, conventional parametric and nonparametric regression techniques are no longer valid. We consider two different approaches to nonparametric regression. The first uses the SIMEX, simulation-extrapolation, method and makes no assumption about the distribution of the unobserved error-prone predictor. For this approach we derive an asymptotic theory for kernel regression which has…

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A chi-squared goodness-of-fit test for logistic regression models based on case-control data

病例对照数据下逻辑回归模型的卡方拟合优度检验

We propose a chi-squared-type statistic to test the validity of the logistic regression model based on case-control data by adapting the goodness-of-fit test of Nikulin-Rao-Robson-Moore. The proposed test statistic requires a high-dimensional matrix inversion, but is otherwise easy to compute and has an asymptotic chi-squared distribution. This test statistic is an alternative to the Kolmogorov-Smirnov-type statistic of Qin & Zhang (1997) and does not need to employ a…

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Interim analyses using repeated confidence bands

使用重复置信带的中期分析

Clinical trials often include interim analyses that compare treatment groups with respect to the mean function of a response process. Sometimes it is unclear how the mean functions of the groups might differ, and thus one cannot confidently prespecify a simple metric upon which a stopping rule or repeated confidence interval can be based. This motivated us to extend the repeated confidence intervals approach for a finite-dimensional parameter (Jennison & Turnbull, 1989) to…

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Miscellanea. A note on tests for nonlinearity in a vector time series

向量时间序列非线性检验札记

A multivariate extension of the univariate nonlinearity test of Tsay (1986) is presented. Simulation results show that the multivariate test is more powerful than its univariate counterpart, especially for series having nonlinear structure involving several components of the vector process and weakly or moderately cross-correlated process error terms. For illustration, the test is applied to a set of seasonally adjusted quarterly capital expenditures and appropriations in…

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Pairwise likelihood methods for inference in image models

图像模型推断的成对似然方法

We consider parameter estimation for certain random set models for image data. For the spatial models considered here, a full likelihood-based approach to this problem is often difficult, since the likelihood generally cannot be computed for images of typical size. This has motivated the consideration of various less computationally demanding parameter estimation techniques. We discuss one method based on likelihoods for small subsets of the data. Consideration of the basic…

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Subsampling and model selection in time series analysis

时间序列分析中的子抽样与模型选择

In this paper the subsampling method of Carlstein (1986) is used to estimate the risk of prediction for time series data. We extend Carlstein's result by proving strong consistency of the subsampling estimator and we propose a procedure for selecting a time series model empirically from a set of possibly nonnested and misspecified models by using estimated risk of prediction as a selection criterion. When this procedure is applied to the selection of the order of an…

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Modelling panels of intercorrelated autoregressive time series

相互相关的自回归时间序列面板建模

We propose a method of modelling panel time series data with both inter- and intra-individual correlation, and of fitting an autoregressive model to such data. Estimators are obtained by a conditional likelihood argument. If there are few observations in each series, the estimators can be dramatically improved by Burg-type estimators taking edge effects into account. The consequences of ignoring the intercorrelation term are analysed. Partial lack of consistency is…

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Dynamic adaptive partitioning for nonlinear time series

非线性时间序列的动态自适应分割

We propose a dynamic adaptive partitioning scheme for nonparametric analysis of stationary nonlinear time series. It yields estimates of the whole probability distribution of the underlying process. We use information from past values to construct adaptive partitioning in a dynamic fashion which is then different from the more common static schemes in the regression set-up. The idea of dynamic partitioning is new. We make it constructive by an approach based on quantisation…

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Semiparametric inference for a quantile comparison function with applications to receiver operating characteristic curves

分位数比较函数的半参数推断及其在受试者工作特征曲线中的应用

In studies to compare two samples, more information may be available on one treatment than the other. When one population is modelled parametrically and the other nonparmetrically, we study large sample properties of a semiparametric sample quantile comparison function and show that it can have substantially smaller asymptotic variance than its nonparametric counterpart, especially near the boundaries. We describe applications to both two-sample tests and receiver operating…

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A Goodness-of-fit Test for Exponentiality Based on the Memoryless Property

基于无记忆性的指数分布拟合优度检验

Summary In this investigation a test of goodness of fit for exponentiality is proposed. This procedure applies equally whether the scale and/or the location parameters of the distribution are known or not. The limiting null and non-null distributions of the test statistic are normal under minimal conditions. Monte Carlo critical values for small sample sizes are given and the power of the test is calculated for various alternatives showing that it compares favourably…

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Biased Bootstrap Methods for Reducing the Effects of Contamination

减轻分布污染影响的有偏 Bootstrap 方法

Summary Contamination of a sampled distribution, for example by a heavy-tailed distribution, can degrade the performance of a statistical estimator. We suggest a general approach to alleviating this problem, using a version of the weighted bootstrap. The idea is to ‘tilt’ away from the contaminated distribution by a given (but arbitrary) amount, in a direction that minimizes a measure of the new distribution's dispersion. This theoretical proposal has a simple empirical…

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Saddlepoint Approximations for the Difference of Order Statistics and Studentized Sample Quantiles

次序统计量之差与学生化样本分位数的鞍点近似

Summary For a sample from a given distribution the difference of two order statistics and the Studentized quantile are statistics whose distribution is needed to obtain tests and confidence intervals for quantiles and quantile differences. This paper gives saddlepoint approximations for densities and saddlepoint approximations of the Lugannani–Rice form for tail probabilities of these statistics. The relative errors of the approximations are n −1 uniformly in a neighbourhood…

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Polynomial Regression and Estimating Functions in the Presence of Multiplicative Measurement Error

乘性测量误差下的多项式回归与估计函数

Summary We consider the polynomial regression model in the presence of multiplicative measurement error in the predictor. Two general methods are considered, with the methods differing in their assumptions about the distributions of the predictor and the measurement errors. Consistent parameter estimates and asymptotic standard errors are derived by using estimating equation theory. Diagnostics are presented for distinguishing additive and multiplicative measurement error.…

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The Covariance Inflation Criterion for Adaptive Model Selection

自适应模型选择的协方差膨胀准则

Summary We propose a new criterion for model selection in prediction problems. The covariance inflation criterion adjusts the training error by the average covariance of the predictions and responses, when the prediction rule is applied to permuted versions of the data set. This criterion can be applied to general prediction problems (e.g. regression or classification) and to general prediction rules (e.g. stepwise regression, tree-based models and neural nets). As a…

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Bayesian Nonparametric Inference for Random Distributions and Related Functions

随机分布及相关函数的贝叶斯非参数推断

Summary In recent years, Bayesian nonparametric inference, both theoretical and computational, has witnessed considerable advances. However, these advances have not received a full critical and comparative analysis of their scope, impact and limitations in statistical modelling; many aspects of the theory and methods remain a mystery to practitioners and many open questions remain. In this paper, we discuss and illustrate the rich modelling and analytic possibilities that…

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Pseudolikelihood Modeling of Multivariate Outcomes in Developmental Toxicology

发育毒理学中多元结局的伪似然建模

The primary goal of this article is to determine benchmark doses based on the ethylene glycol study, which comprises data from a developmental toxicity study in mice. Because the data involve a vector of malformation indicators, a flexible model for multivariate clustered data is required. An exponential family model is considered and pseudolikelihood-based inferential tools are proposed, hence avoiding excessive computational requirements.

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Combining Data from Polymerase Chain Reaction DNA Typing Experiments: Applications to Sperm Typing Data

合并 PCR DNA 分型实验数据:精子分型的应用

The polymerase chain reaction (PCR) is a procedure by which the DNA in a single cell can be made to replicate many times in a test tube. By amplifying the DNA from individual sperm cells and typing the results, estimates of male recombination fractions can be made, which are valuable for creating genetic maps and locating regions of unusually intense crossover activity on the human genome. Because PCR typing results are subject to random error, stochastic models must be…

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Nonparametric Two-Sample Comparisons of Changes on Ordinal Responses

有序响应变化的非参数两样本比较

We discuss two-armed clinical trials in which an ordinal response is observed on entry and at follow-up. In certain subject areas, such data are often reduced to the counts of patients who have deteriorated, remained the same, or improved. We develop asymptotic distribution theory to compare this approach to the use of nonparametric tests based on poststratification of the follow-up responses according to the entry levels. The theory for the latter approach is established by…

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