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平滑分位数比估计AI 解读

We propose a novel approach to estimating the mean difference between two highly skewed distributions. The method, which we call smooth quantile ratio estimation, smooths, over percentiles, the ratio of the quantiles of the two distributions. The method defines a large class of estimators, including the sample mean difference, the maximum likelihood estimator under log-normal samples and the L-estimator. We derive asymptotic properties such as consistency and asymptotic…

Lorelogram：探索纵向分类响应依赖性的回归方法AI 解读

We propose flexible regression estimators of the marginal pairwise log-odds ratio measure of association for longitudinal categorical responses. The function that we estimate is the log-odds ratio analog of the correlogram; hence we name the function the lorelogram. Measuring the association of categorical responses on the log-odds scale allows ease of interpretation and allows pairwise association to remain unconstrained by the marginal means, a feature not shared by…

多重离散结果的潜变量回归AI 解读

Quantifying human health and functioning poses significant challenges in many research areas. Commonly in the social and behavioral sciences and increasingly in epidemiologic research, multiple indicators are utilized as responses in lieu of an obvious single measure for an outcome of interest. In this article we study the concomitant latent class model for analyzing such multivariate categorical outcome data. We develop practical theory for reducing and identifying such…

聚类有序测量的边际回归模型AI 解读

This article constructs statistical models for clustered ordinal measurements. We specify two regression models: one for the marginal means and one for the marginal pairwise global odds ratios. Of particular interest are problems in which the odds ratio regression is a focus. Simple assumptions about higher-order conditional moments give a quadratic exponential likelihood function with second-order estimating equations (GEE2) as score equations. But computational difficulty…

利用高斯混合平滑估计混合分布AI 解读

We propose a method of estimating mixing distributions using maximum likelihood over the class of arbitrary mixtures of Gaussians subject to the constraint that the component variances be greater than or equal to some minimum value h. This approach can lead to estimates of many shapes, with smoothness controlled by parameter h. We show that the resulting estimate will always be a finite mixture of Gaussians, each having variance h. The nonparametric maximum likelihood…

用交替逻辑回归建模多元二元数据AI 解读

Marginal models for multivariate binary data permit separate modelling of the relationship of the response with explanatory variables, and the association between pairs of responses. When the former is the scientific focus, a first-order generalized estimating equation method (Liang & Zeger, 1986) is easy to implement and gives efficient estimates of regression coefficients, although estimates of the association among the binary outcomes can be inefficient. When the…

分类数据的多元回归分析AI 解读

SUMMARY It is common to observe a vector of discrete and/or continuous responses in scientific problems where the objective is to characterize the dependence of each response on explanatory variables and to account for the association between the outcomes. The response vector can comprise repeated observations on one variable, as in longitudinal studies or genetic studies of families, or can include observations for different variables. This paper discusses a class of models…

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带脉冲时间序列的非高斯模型AI 解读

A non-Gaussian autoregressive-like model is presented for time series that exhibit occasional large increases in value, termed pulses, and exponential decay between pulses. The model differs from a first-order autoregressive process in its incorporation of feedback between the distribution of the current innovation and the history of the process. Likelihood-based methods of inference for the model are developed, and an application to endocrinological data is given.

多元二元时间序列的一类 logistic 回归模型AI 解读

A logistic model for multivariate binary time series is proposed. First, we establish the equivalence between a log-linear model for the marginal distribution of a multivariate binary random vector and logistic models for the conditional distributions of each component given the others. The logistic formulation is used to describe a Markov chain for each series, which implies a Markov model for the vector of time series. A pseudolikelihood estimation procedure is presented.…

含脉冲时间序列的非高斯模型AI 解读

A non-Gaussian autoregressive-like model is presented for time series that exhibit occasional large increases in value, termed pulses, and exponential decay between pulses. The model differs from a first-order autoregressive process in its incorporation of feedback between the distribution of the current innovation and the history of the process. Likelihood-based methods of inference for the model are developed, and an application to endocrinological data is given.

计数时间序列的回归模型AI 解读

This paper discusses a model for regression analysis with a time series of counts. Correlation is assumed to arise from an unobservable process added to the linear predictor in a log linear model. An estimating equation approach used for parameter estimation leads to an iterative weighted and filtered least-squares algorithm. Asymptotic properties for the regression coefficients are presented. We illustrate the technique with an analysis of trends in U.S. polio incidence…

频域中位数时间序列AI 解读

This article discusses the median of a batch of time series. We focus on the frequency-domain median (FDM) series (Zeger 1985), which is calculated from the “mediancenters” (Gower 1974) of the discrete Fourier transforms. Like the univariate median, it has a breakdown value of 50%. Like the mean, the FDM time series of independent realizations from a stationary Gaussian process has a spectrum proportional to that of the component series. Analogs of the univariate median have…

频域中位数时间序列AI 解读

This article discusses the median of a batch of time series. We focus on the frequency-domain median (FDM) series (Zeger 1985), which is calculated from the “mediancenters” (Gower 1974) of the discrete Fourier transforms. Like the univariate median, it has a breakdown value of 50%. Like the mean, the FDM time series of independent realizations from a stationary Gaussian process has a spectrum proportional to that of the component series. Analogs of the univariate median have…

删失且自相关数据的回归分析AI 解读

For many studies in which data are collected sequentially in time, the sensitivity of the measurement is limited and an exact value can be recorded only if it falls within a specified range. This gives rise to a censored time series. In this article, we present a methodology for regression analysis of censored time series data. We fit autoregressive models to account for the time dependence. Two numerical methods for full likelihood estimation and an approximate method are…

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使用广义线性模型分析纵向数据AI 解读

This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for niultivariate Gaussian outcomes. Asymptotic theory…

在频域中探索臭氧的空间时间序列AI 解读

In 1974, atmospheric scientists (Molina and Rowland; Cicerone et al.) first postulated that man-made chemicals known as CFC's (e.g., freon) deplete the earth's ozone when released into the atmosphere. This article presents an exploratory data analysis (EDA) of a spatial time series of ozone measurements. Frequency domain techniques for the EDA are introduced and used to graphically display global, long-term trends relevant to the CFC issue. My intention is to illustrate the…

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具有时间不变协变量的二元纵向数据分析AI 解读

This paper considers extensions of logistic regression to the case where the binary outcome variable is observed repeatedly for each subject. We propose two working models that lead to consistent estimates of the regression parameters and of their variances under mild assumptions about the time dependence within each subject's data. The efficiency of the proposed estimators is examined. An analysis of stress in mothers with infants is presented to illustrate the proposed…