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加权重复中位数平滑与滤波AI 解读

We propose weighted repeated median filters and smoothers for robust nonparametric regression in general and for robust online signal extraction from time series in particular. The new methods allow us to remove outlying sequences and to preserve discontinuities (shifts) in the underlying regression function (the signal) in the presence of local linear trends. Suitable weighting of the observations according to their distances in the design space reduces the bias arising…

稳健似然累积和控制图AI 解读

In practice, the cumulative sum (CUSUM) control chart is often used to detect small shifts in the mean of a normally distributed process, but it performs poorly for thick-tailed processes and for large shifts. This article provides a robust-likelihood cumulative sum (RLCUSUM) chart that discounts outliers and yet has the ability to detect large shifts quickly. The new chart is motivated by the likelihood underpinnings of the CUSUM. It is based on the likelihood of a variate…

Brock–Dechert–Scheinkman检验的秩扩展AI 解读

This article proposes new tests of randomness for innovations in a large class of time series models. These tests are based on functionals of empirical processes constructed from either the model residuals or their associated ranks. The asymptotic behavior of these processes is determined under the null hypothesis of randomness. The limiting distributions are seen to be independent of estimation errors under appropriate regularity conditions. Several test statistics are…

协变量随机缺失回归模型的筛极大似然估计AI 解读

Missing covariates are common in regression problems. We propose a new semiparametric method based on a fully nonparametric distribution for the missing covariates that are assumed to be missing at random. The method of sieve maximum likelihood estimation is used to obtain the estimators of the regression coefficients. These estimators are shown to be consistent and asymptotically normal with their asymptotic covariance matrix that achieves the semiparametric efficiency…

异方差测量误差下的非参数回归估计AI 解读

In the classical errors-in-variables problem, the goal is to estimate a regression curve from data in which the explanatory variable is measured with error. In this context, nonparametric methods have been proposed that rely on the assumption that the measurement errors are identically distributed. Although there are many situations in which this assumption is too restrictive, nonparametric estimators in the more realistic setting of heteroscedastic errors have not been…

正态、Student t与bootstrap校准可支持多少个同时假设检验？AI 解读

In the analysis of microarray data, and in some other contemporary statistical problems, it is not uncommon to apply hypothesis tests in a highly simultaneous way. The number, N say, of tests used can be much larger than the sample sizes, n, to which the tests are applied, yet we wish to calibrate the tests so that the overall level of the simultaneous test is accurate. Often the sampling distribution is quite different for each test, so there may not be an opportunity to…

多重插补的多种适用情境与合并规则AI 解读

Multiple imputation was first conceived as a tool that statistical agencies could use to handle nonresponse in large-sample public use surveys. In the last two decades, the multiple-imputation framework has been adapted for other statistical contexts. For example, individual researchers use multiple imputation to handle missing data in small samples, statistical agencies disseminate multiply-imputed data sets for purposes of protecting data confidentiality, and survey…

非齐次空间点模式的稀疏化分块自助方差估计AI 解读

When modeling inhomogeneous spatial point patterns, it is of interest to fit a parametric model for the first-order intensity function (FOIF) of the process in terms of some measured covariates. Estimates for the regression coefficients, say , can be obtained by maximizing a Poisson maximum likelihood criterion. Little work has been done on the asymptotic distribution of except in some special cases. In this article we show that is asymptotically normal for a general class…

非参数函数估计的非平衡Haar技术AI 解读

The discrete unbalanced Haar (UH) transform is a decomposition of one-dimensional data with respect to an orthonormal Haar-like basis where jumps in the basis vectors do not necessarily occur in the middle of their support. We introduce a multiscale procedure for estimation in Gaussian noise that consists of three steps: a UH transform, thresholding of the decomposition coefficients, and the inverse UH transform. We show that our estimator is mean squared consistent with…

扩散加权磁共振成像中扩散张量的统计分析AI 解读

Diffusion tensor imaging has been widely used to reconstruct the structure and orientation of fibers in biological tissues, particularly in the white matter of the brain, because it can track the effective diffusion of water along those fibers. The raw diffusion-weighted images from which diffusion tensors are estimated, however, inherently contain noise. Noise in the images produces uncertainty in the estimation of the tensors (which are 3 × 3 positive-definite matrices)…

等位基因丢失频率数据的贝叶斯分析AI 解读

One objective of allelic-loss studies is to identify chromosomal locations that may harbor tumor-suppressor genes. An instability-selection model has been developed for allelic-loss data in which the loss events are available for each tumor and each marker (allelotypes). In performing pooled analyses of published allelic-loss experiments, however, only summaries of the frequency of allelic loss (FAL) may be available. The instability-selection model can be applied to these…

非齐次泊松过程的贝叶斯预测及呼叫中心应用AI 解读

A call center is a centralized hub where customer and other telephone calls are handled by an organization. In today's economy, call centers have become the primary points of contact between customers and businesses. Thus accurate predictions of call arrival rates are indispensable to help call center practitioners staff their call centers efficiently and cost-effectively. This article proposes a multiplicative model for modeling and forecasting within-day arrival rates to a…

未知损失函数下的预测最优性检验AI 解读

Empirical tests of forecast optimality have traditionally been conducted under the assumption of mean squared error loss or some other known loss function. In this article we establish new testable properties that hold when the forecaster's loss function is unknown but testable restrictions can be imposed on the data-generating process, trading off conditions on the data-generating process against conditions on the loss function. We propose flexible estimation of the…

点样微阵列图像的局部平滑分割AI 解读

Gene microarray data are used in a wide variety of applications, including pharmaceutical and clinical research. By comparing gene expression in normal and abnormal cells, microarrays can be used to identify genes involved in particular diseases, and these genes then can be targeted by therapeutic drugs. Most gene expression data are produced from spotted microarray images. A spotted microarray image consists of thousands of spots, with individual DNA sequences first printed…

多中心生存研究的多分辨率风险模型AI 解读

In multicenter studies, one often needs to make inference about a population survival curve based on multiple, possibly heterogeneous survival data from individual centers. We investigate a flexible Bayesian method for estimating a population survival curve based on a semiparametric multiresolution hazard model that can incorporate covariates and account for center heterogeneity. The method yields a smooth estimate of the survival curve for "multiple resolutions" or time…

贝叶斯多元保序回归样条AI 解读

In many applications, interest focuses on assessing the relationship between a predictor and a multivariate outcome variable, and there may be prior knowledge about the shape of the regression curves. For example, regression functions that relate dose of a possible risk factor to different adverse outcomes can often be assumed to be nondecreasing. In such cases, interest focuses on (1) assessing evidence of an overall adverse effect, (2) determining which outcomes are most…

双变量竞争风险数据的非参数关联分析AI 解读

Although nonparametric analyses of bivariate failure times under independent censoring have been widely studied, nonparametric analyses of bivariate competing-risks data have not yet been investigated. Such analyses are important in familial association studies, where multiple interacting failure types may violate the independent censoring assumption. We develop nonparametric estimators for the bivariate cause-specific hazards function and the bivariate cumulative incidence…

函数型观测独立性的 Portmanteau 综合检验AI 解读

In various fields, observations are curves over some natural time interval. These curves often arise from finely spaced measurements (e.g., in physical sciences and finance) or from smoothing unequally spaced sparse measurements. Recent years have seen the development of tools for analyzing such data in the growing field of functional data analysis. To validate the assumptions underlying these tools, it is important to verify that the functional observations form a simple…

区域及区域内信息性概率抽样下的小区域估计AI 解读

In this article we show how to predict small-area means and obtain valid mean squared error estimators and confidence intervals when the areas represented in the sample are sampled with unequal probabilities possibly related to the true (unknown) area means and the sampling of units within the selected areas is with probabilities possibly related to the outcome values. Ignoring the effects of the sampling process on the distribution of the observed outcomes in such cases may…

加速失效时间模型的有效估计AI 解读

The accelerated failure time model provides a natural formulation of the effects of covariates on potentially censored response variable. The existing semiparametric estimators are computationally intractable and statistically inefficient. In this article we propose an approximate nonparametric maximum likelihood method for the accelerated failure time model with possibly time-dependent covariates. We estimate the regression parameters by maximizing a kernel-smoothed profile…

高频金融数据的多尺度跳跃与波动率分析AI 解读

The wide availability of high-frequency data for many financial instruments stimulates an upsurge interest in statistical research on the estimation of volatility. Jump-diffusion processes observed with market microstructure noise are frequently used to model high-frequency financial data. Yet existing methods are developed for either noisy data from a continuous-diffusion price model or data from a jump-diffusion price model without noise. We propose methods to cope with…

相关误差下惩罚样条平滑的一则说明AI 解读

We investigate the behavior of data-driven smoothing parameters for penalized spline regression in the presence of correlated data. It has been shown for other smoothing methods that mean squared error minimizers, such as (generalized) cross-validation or the Akaike information criterion, are extremely sensitive to misspecifications of the correlation structure resulting in over- or (under-)fitting the data. In contrast to this, we show that a maximum likelihood–based choice…

基于最小角回归的稳健线性模型选择AI 解读

In this article we consider the problem of building a linear prediction model when the number of candidate predictors is large and the data possibly contain anomalies that are difficult to visualize and clean. We want to predict the nonoutlying cases; therefore, we need a method that is simultaneously robust and scalable. We consider the stepwise least angle regression (LARS) algorithm which is computationally very efficient but sensitive to outliers. We introduce two…