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具有自回归与异方差扰动的动态空间自回归模型AI 解读

Summary We propose a new class of models specifically tailored for spatiotemporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, that is, SARAR(1, 1), by exploiting the recent advancements in score‐driven (SD) models typically used in time series econometrics. In particular, we allow for time‐varying spatial autoregressive coefficients as well as time‐varying regressor coefficients and…

干扰模型中普遍最优圆周设计的判别AI 解读

Many applications of block designs exhibit neighbor and edge effects. A popular remedy is to use the circular design coupled with the interference model. The search for optimal or efficient designs has been intensively studied in recent years. The circular neighbor balanced designs at distances 1 and 2 (CNBD2), including orthogonal array of type I ($\mathrm{OA}_{I}$) of strength $2$, are the two major designs proposed in literature for the purpose of estimating the direct…

气候变化下基于空间模型的最优种源配置：美国东南部火炬松应用AI 解读

Provenance tests are a common tool in forestry designed to identify superior genotypes for planting at specific locations. The trials are replicated experiments established with seed from parent trees collected from different regions and grown at several locations. In this work, a Bayesian spatial approach is developed for modeling the expected relative performance of seed sources using climate variables as predictors associated with the origin of seed source and the…

多尺度空间密度平滑：大规模辐射调查与异常检测应用AI 解读

We consider the problem of estimating a spatially varying density function, motivated by problems that arise in large-scale radiological survey and anomaly detection. In this context, the density functions to be estimated are the background gamma-ray energy spectra at sites spread across a large geographical area, such as nuclear production and waste-storage sites, military bases, medical facilities, university campuses, or the downtown of a city. Several challenges combine…

MWPCR：用于高维预测的多尺度加权主成分回归AI 解读

We propose a multiscale weighted principal component regression (MWPCR) framework for the use of high dimensional features with strong spatial features (e.g., smoothness and correlation) to predict an outcome variable, such as disease status. This development is motivated by identifying imaging biomarkers that could potentially aid detection, diagnosis, assessment of prognosis, prediction of response to treatment, and monitoring of disease status, among many others. The…

海量基因组数据挖掘：半参数主题建模方法AI 解读

Characterizing the functional relevance of transcription factors (TFs) in different biological contexts is pivotal in systems biology. Given the massive amount of genomic data, computational identification of TFs is emerging as a useful approach to bridge functional genomics with disease risk loci. In this article, we use large-scale gene expression and chromatin immunoprecipitation (ChIP) data corpuses to conduct high-throughput TF-biological context association analysis.…

利用链式追踪抽样估计总体规模AI 解读

We present a new design and method for estimating the size of a hidden population best reached through a link-tracing design. The design is based on selecting initial samples at random and then adaptively tracing links to add new members. The inferential procedure involves the Rao–Blackwell theorem applied to a sufficient statistic markedly different from the usual one that arises in sampling from a finite population. The strategy involves a combination of link-tracing and…

Tukey g-and-h 随机场AI 解读

We propose a new class of transGaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable transformation for the dataset of interest while estimating model parameters.…

海量数据的广义加性模型：英国黑烟监测网日度数据建模AI 解读

We develop scalable methods for fitting penalized regression spline based generalized additive models with of the order of 104 coefficients to up to 108 data. Computational feasibility rests on: (i) a new iteration scheme for estimation of model coefficients and smoothing parameters, avoiding poorly scaling matrix operations; (ii) parallelization of the iteration’s pivoted block Cholesky and basic matrix operations; (iii) the marginal discretization of model covariates to…

网络实验中单调处理效应的估计AI 解读

Randomized experiments on social networks pose statistical challenges, due to the possibility of interference between units. We propose new methods for finding confidence intervals on the attributable treatment effect in such settings. The methods do not require partial interference, but instead require an identifying assumption that is similar to requiring nonnegative treatment effects. Network or spatial information can be used to customize the test statistic; in…

平滑系数空间自回归模型的半参数估计与检验AI 解读

This paper considers a flexible semiparametric spatial autoregressive (mixed-regressive) model in which unknown coefficients are permitted to be nonparametric functions of some contextual variables to allow for potential nonlinearities and parameter heterogeneity in the spatial relationship. Unlike other semiparametric spatial dependence models, ours permits the spatial autoregressive parameter to meaningfully vary across units and thus allows the identification of a…

引力模型的双向效应估计AI 解读

Empirical models for dyadic interactions between n agents often feature agent-specific parameters. Fixed-effect estimators of such models generally have bias of order n−1, which is nonnegligible relative to their standard error. Therefore, confidence sets based on the asymptotic distribution have incorrect coverage. This paper looks at models with multiplicative unobservables and fixed effects. We derive moment conditions that are free of fixed effects and use them to set up…

协变量辅助的谱聚类AI 解读

Biological and social systems consist of myriad interacting units. The interactions can be represented in the form of a graph or network. Measurements of these graphs can reveal the underlying structure of these interactions, which provides insight into the systems that generated the graphs. Moreover, in applications such as connectomics, social networks, and genomics, graph data are accompanied by contextualizing measures on each node. We utilize these node covariates to…

Matérn排斥点过程的贝叶斯推断AI 解读

Summary In many applications involving point pattern data, the Poisson process assumption is unrealistic, with the data exhibiting a more regular spread. Such repulsion between events is exhibited by trees for example, because of competition for light and nutrients. Other examples include the locations of biological cells and cities, and the times of neuronal spikes. Given the many applications of repulsive point processes, there is a surprisingly limited literature…

空间回归的加性模型构建AI 解读

Summary Spatial regression is an important predictive tool in many scientific applications and an additive model provides a flexible regression relationship between predictors and a response variable. We develop a regularized variable selection technique for building a spatial additive model. We find that the methods developed for independent data do not work well for spatially dependent data. This motivates us to propose a spatially weighted l2-error norm with a group lasso…

马尔可夫网络直接稀疏变化学习的支持一致性AI 解读

We study the problem of learning sparse structure changes between two Markov networks $P$ and $Q$. Rather than fitting two Markov networks separately to two sets of data and figuring out their differences, a recent work proposed to learn changes directly via estimating the ratio between two Markov network models. In this paper, we give sufficient conditions for successful change detection with respect to the sample size $n_{p},n_{q}$, the dimension of data $m$ and the number…

网络向量自回归AI 解读

We consider here a large-scale social network with a continuous response observed for each node at equally spaced time points. The responses from different nodes constitute an ultra-high dimensional vector, whose time series dynamic is to be investigated. In addition, the network structure is also taken into consideration, for which we propose a network vector autoregressive (NAR) model. The NAR model assumes each node’s response at a given time point as a linear combination…

空间自回归中的间接推断AI 解读

Ordinary least‐squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). In this paper, we explore the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and…

固定效应动态空间面板模型与脉冲响应分析AI 解读

Real data often have complicated correlations over cross section and time. Such correlations are of particular interests in empirical studies. This paper considers using high order spatial lags and high order time lags to model complicated correlations over cross section and time. We propose to use the quasi maximum likelihood (QML) method to estimate the model. We establish the asymptotic theory of the quasi maximum likelihood estimator (QMLE), including the consistency and…

动物运动的基函数模型AI 解读

Advances in satellite-based data collection techniques have served as a catalyst for new statistical methodology to analyze these data. In wildlife ecological studies, satellite-based data and methodology have provided a wealth of information about animal space use and the investigation of individual-based animal–environment relationships. With the technology for data collection improving dramatically over time, we are left with massive archives of historical animal…

利用时空随机游走的极限分布建模空间协方差AI 解读

We present an approach for modeling areal spatial covariance in observed genetic allele data by considering the stationary (limiting) distribution of a spatio-temporal Markov random walk model for gene flow. This stationary distribution corresponds to an intrinsic simultaneous autoregressive (SAR) model for spatial correlation, and provides a principled approach to specifying areal spatial models when a spatio-temporal generating process can be assumed. We apply the approach…

不可忽略缺失下估计 HIV 流行率的联立方程方法AI 解读

Estimates of HIV prevalence are important for policy to establish the health status of a country’s population and to evaluate the effectiveness of population-based interventions and campaigns. However, participation rates in testing for surveillance conducted as part of household surveys, on which many of these estimates are based, can be low. HIV positive individuals may be less likely to participate because they fear disclosure, in which case estimates obtained using…

含协变量的贝叶斯层级多种群多状态 Jolly–Seber 模型AI 解读

Estimating abundance for multiple populations is of fundamental importance to many ecological monitoring programs. Equally important is quantifying the spatial distribution and characterizing the migratory behavior of target populations within the study domain. To achieve these goals, we propose a Bayesian hierarchical multi-population multistate Jolly–Seber model that incorporates covariates. The model is proposed using a state-space framework and has several distinct…

基于均值、空间符号与空间秩的高维数据检验AI 解读

Tests based on mean vectors and spatial signs and ranks for a zero mean in one-sample problems and for the equality of means in two-sample problems have been studied in the recent literature for high-dimensional data with the dimension larger than the sample size. For the above testing problems, we show that under suitable sequences of alternatives, the powers of the mean-based tests and the tests based on spatial signs and ranks tend to be same as the data dimension tends…