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增广粒子滤波AI 解读

Particle filters have been widely used for online filtering problems in state–space models (SSMs). The current available proposal distributions depend either only on the state dynamics, or only on the observation, or on both sources of information but are not available for general SSMs. In this article, we develop a new particle filtering algorithm, called the augmented particle filter (APF), for online filtering problems in SSMs. The APF combines two sets of particles from…

稳健跳跃回归AI 解读

We develop robust inference methods for studying linear dependence between the jumps of discretely observed processes at high frequency. Unlike classical linear regressions, jump regressions are determined by a small number of jumps occurring over a fixed time interval and the rest of the components of the processes around the jump times. The latter are the continuous martingale parts of the processes as well as observation noise. By sampling more frequently the role of…

通过惩罚促进整合分析中的稀疏结构相似性AI 解读

For data with high-dimensional covariates but small sample sizes, the analysis of single datasets often generates unsatisfactory results. The integrative analysis of multiple independent datasets provides an effective way of pooling information and outperforms single-dataset and several alternative multi-datasets methods. Under many scenarios, multiple datasets are expected to share common important covariates, that is, the corresponding models have similarity in their…

高维协方差矩阵的动态结构及其在投资组合配置中的应用AI 解读

Shaojun Guoa, John Leigh Boxb & Wenyang Zhangb*a Institute of Statistics and Big Data, Renmin University of China, Beijing, Chinab Department of Mathematics, University of York, York, United KingdomCONTACT Wenyang Zhang wenyang.zhang@york.ac.uk Department of Mathematics, The University of York, York YO10 5DD, United KingdomSupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.ABSTRACTEstimation of high-dimensional covariance…

泊松数据的动态多尺度时空模型AI 解读

We propose a new class of dynamic multiscale models for Poisson spatiotemporal processes. Specifically, we use a multiscale spatial Poisson factorization to decompose the Poisson process at each time point into spatiotemporal multiscale coefficients. We then connect these spatiotemporal multiscale coefficients through time with a novel Dirichlet evolution. Further, we propose a simulation-based full Bayesian posterior analysis. In particular, we develop filtering equations…

用于高维估计的非局部先验AI 解读

Jointly achieving parsimony and good predictive power in high dimensions is a main challenge in statistics. Nonlocal priors (NLPs) possess appealing properties for model choice, but their use for estimation has not been studied in detail. We show that for regular models NLP-based Bayesian model averaging (BMA) shrink spurious parameters either at fast polynomial or quasi-exponential rates as the sample size n increases, while nonspurious parameter estimates are not shrunk.…

检测高维协方差矩阵微弱与稀疏差异的加权统计量AI 解读

This article considers testing equality of two population covariance matrices when the data dimension p diverges with the sample size n (p/n → c > 0). We propose a weighted test statistic that is data-driven and powerful in both faint alternatives (many small disturbances) and sparse alternatives (several large disturbances). Its asymptotic null distribution is derived by large random matrix theory without assuming the existence of a limiting cumulative distribution function…

用于估计个体化治疗规则的残差加权学习AI 解读

Personalized medicine has received increasing attention among statisticians, computer scientists, and clinical practitioners. A major component of personalized medicine is the estimation of individualized treatment rules (ITRs). Recently, Zhao et al. (2012) proposed outcome weighted learning (OWL) to construct ITRs that directly optimize the clinical outcome. Although OWL opens the door to introducing machine learning techniques to optimal treatment regimes, it still has…

稳健非正规极大似然：调优、计算及与其他稳健高斯聚类方法的比较AI 解读

The two main topics of this article are the introduction of the “optimally tuned robust improper maximum likelihood estimator” (OTRIMLE) for robust clustering based on the multivariate Gaussian model for clusters, and a comprehensive simulation study comparing the OTRIMLE to maximum likelihood in Gaussian mixtures with and without noise component, mixtures of t-distributions, and the TCLUST approach for trimmed clustering. The OTRIMLE uses an improper constant density for…

高阶马尔可夫链的贝叶斯非参数建模AI 解读

We consider the problem of flexible modeling of higher order Markov chains when an upper bound on the order of the chain is known but the true order and nature of the serial dependence are unknown. We propose Bayesian nonparametric methodology based on conditional tensor factorizations, which can characterize any transition probability with a specified maximal order. The methodology selects the important lags and captures higher order interactions among the lags, while also…

扩散 MRI 模型中的低信噪比问题AI 解读

Noise is a common issue for all magnetic resonance imaging (MRI) techniques such as diffusion MRI and obviously leads to variability of the estimates in any model describing the data. Increasing spatial resolution in MR experiments further diminishes the signal-to-noise ratio (SNR). However, with low SNR the expected signal deviates from the true value. Common modeling approaches therefore lead to a bias in estimated model parameters. Adjustments require an analysis of the…

通过具有局部依赖的贝叶斯混合模型多重插补分类与连续缺失值AI 解读

We present a nonparametric Bayesian joint model for multivariate continuous and categorical variables, with the intention of developing a flexible engine for multiple imputation of missing values. The model fuses Dirichlet process mixtures of multinomial distributions for categorical variables with Dirichlet process mixtures of multivariate normal distributions for continuous variables. We incorporate dependence between the continuous and categorical variables by (1)…

一般平滑模型的平滑参数与模型选择AI 解读

This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known problem with AIC for such models, thereby improving the range of…

温度风险的局部建模AI 解读

On the temperature derivative market, modeling temperature volatility is an important issue for pricing and hedging. To apply the pricing tools of financial mathematics, one needs to isolate a Gaussian risk factor. A conventional model for temperature dynamics is a stochastic model with seasonality and intertemporal autocorrelation. Empirical work based on seasonality and autocorrelation correction reveals that the obtained residuals are heteroscedastic with a periodic…

融合已知与新生物信息的层级特征选择：识别前列腺癌复发相关基因组特征AI 解读

Our work is motivated by a prostate cancer study aimed at identifying mRNA and miRNA biomarkers that are predictive of cancer recurrence after prostatectomy. It has been shown in the literature that incorporating known biological information on pathway memberships and interactions among biomarkers improves feature selection of high-dimensional biomarkers in relation to disease risk. Biological information is often represented by graphs or networks, in which biomarkers are…

戒烟研究联合模型中不可忽略退出的贝叶斯方法AI 解读

Inference on data with missingness can be challenging, particularly if the knowledge that a measurement was unobserved provides information about its distribution. Our work is motivated by the Commit to Quit II study, a smoking cessation trial that measured smoking status and weight change as weekly outcomes. It is expected that dropout in this study was informative and that patients with missed measurements are more likely to be smoking, even after conditioning on their…

关联学习实验中动态脑过程演化的建模AI 解读

We develop a new time series model to investigate the dynamic interactions between the nucleus accumbens and the hippocampus during an associative learning experiment. Preliminary analyses indicated that the spectral properties of the local field potentials at these two regions changed over the trials of the experiment. While many models already take into account nonstationarity within a single trial, the evolution of the dynamics across trials is often ignored. Our proposed…

生物流体单粒子追踪的模型比较与评估AI 解读

State-of-the-art techniques in passive particle-tracking microscopy provide high-resolution path trajectories of diverse foreign particles in biological fluids. For particles on the order of 1 μm diameter, these paths are generally inconsistent with simple Brownian motion. Yet, despite an abundance of data confirming these findings and their wide-ranging scientific implications, stochastic modeling of the complex particle motion has received comparatively little attention.…

广义线性模型与广义线性混合效应模型的最优模型平均估计AI 解读

Considering model averaging estimation in generalized linear models, we propose a weight choice criterion based on the Kullback–Leibler (KL) loss with a penalty term. This criterion is different from that for continuous observations in principle, but reduces to the Mallows criterion in the situation. We prove that the corresponding model averaging estimator is asymptotically optimal under certain assumptions. We further extend our concern to the generalized linear…

含交互固定效应与多重结构突变的面板数据模型AI 解读

In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the…

利用含测量误差协变量的函数进行因果匹配与加权AI 解读

Matching estimators are commonly used to estimate causal effects in nonexperimental settings. Covariate measurement error can be problematic for matching estimators when observational treatment groups differ on latent quantities observed only through error-prone surrogates. We establish necessary and sufficient conditions for matching and weighting with functions of observed covariates to yield unconfounded causal effect estimators, generalizing results from the standard…

时空点过程模型的稀疏近似推断AI 解读

Spatio-temporal log-Gaussian Cox process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computationally challenging both due to the high-resolution modeling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of (spatially) discretized log-Gaussian Cox process models by using approximate message-passing algorithms. The…

线性混合模型的复合稳健估计量AI 解读

The classical Tukey–Huber contamination model (CCM) is a commonly adopted framework to describe the mechanism of outliers generation in robust statistics. Given a dataset with n observations and p variables, under the CCM, an outlier is a unit, even if only one or a few values are corrupted. Classical robust procedures were designed to cope with this type of outliers. Recently, a new mechanism of outlier generation was introduced, namely, the independent contamination model…

复杂调查数据的统计匹配分析及应用AI 解读

The goal of statistical matching is the estimation of a joint distribution having observed only samples from its marginals. The lack of joint observations on the variables of interest is the reason of uncertainty about the joint population distribution function. In the present article, the notion of matching error is introduced, and upper-bounded via an appropriate measure of uncertainty. Then, an estimate of the distribution function for the variables not jointly observed…