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资源有限环境下患病率制图的模型化地统计方法AI 解读

In low-resource settings, prevalence mapping relies on empirical prevalence data from a finite, often spatially sparse, set of surveys of communities within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model with binomial error distribution, logistic link, and a combination of explanatory variables and a…

基于低秩模型的非齐次时空对数高斯 Cox 过程贝叶斯估计与预测：犯罪监测应用AI 解读

In this article, we propose a method for conducting likelihood-based inference for a class of nonstationary spatiotemporal log-Gaussian Cox processes. The method uses convolution-based models to capture spatiotemporal correlation structure, is computationally feasible even for large datasets, and does not require knowledge of the underlying spatial intensity of the process. We describe an application to a surveillance system for detecting emergent spatiotemporal clusters of…

利用风险人口的空间汇总信息估计个体风险AI 解读

We propose a novel alternative to case-control sampling for the estimation of individual-level risk in spatial epidemiology. Our approach uses weighted estimating equations to estimate regression parameters in the intensity function of an inhomogeneous spatial point process, when information on risk-factors is available at the individual level for cases, but only at a spatially aggregated level for the population at risk. We develop data-driven methods to select the weights…

检测标记点过程中的标记与位置依赖AI 解读

Summary We introduce two characteristics for stationary and isotropic marked point proces- ses, E(h) and V(h), and describe their use in investigating mark–point interactions. These quantities are functions of the interpoint distance h and denote the conditional expectation and the conditional variance of a mark respectively, given that there is a further point of the process a distance h away. We present tests based on E and V for the hypothesis that the values of the marks…

对数高斯 Cox 过程的时空预测AI 解读

Summary Space–time point pattern data have become more widely available as a result of technological developments in areas such as geographic information systems. We describe a flexible class of space–time point processes. Our models are Cox processes whose stochastic intensity is a space–time Ornstein–Uhlenbeck process. We develop moment-based methods of parameter estimation, show how to predict the underlying intensity by using a Markov chain Monte Carlo approach and…

密度估计非参数反卷积的傅里叶方法AI 解读

SUMMARY We consider the problem of constructing a nonparametric estimate of a probability density function h from independent random samples of observations from densities a and f, when a represents the convolution of h and f. Our approach is based on truncated Fourier inversion, in which the truncation point plays the role of a smoothing parameter. We derive the asymptotic mean integrated squared error of the estimate and use this formula to suggest a simple practical…

摘要暂缺

临床神经解剖学中重复空间点模式的方差分析AI 解读

Our application concerns the analysis of the spatial distributions of pyramidal neurons in the cingulate cortex of human subjects in three diagnostic groupings. Our purpose is to develop and apply several methods for the analysis of replicated spatial point patterns to identify anatomical differences between normal, schizoaffective, and schizophrenic persons at the time of death. We begin with a scaled Poisson analysis that uncovers significant differences between the groups…

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.

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.

成对相互作用点过程的非参数估计量AI 解读

The paper develops a nonparametric estimator for the class of pairwise-interaction point processes. The estimator is based on an approximation in statistical physics due to Percus and Yevick, which relates the interaction function of a pairwise-interaction point process to its second-order statistics via an integral equation. The results of a simulation study are presented.

成对相互作用点过程的非参数估计AI 解读

The paper develops a nonparametric estimator for the class of pairwise-interaction point processes. The estimator is based on an approximation in statistical physics due to Percus and Yevick, which relates the interaction function of a pairwise-interaction point process to its second-order statistics via an integral equation. The results of a simulation study are presented.

正交级数密度估计中的项选择AI 解读

We show that Kronmal and Tarter's well-known rule for selecting the terms in an orthogonal series density estimator can lead to poor performance and even inconsistency in certain cases. These difficulties arise when the underlying density has a nonmonotone sequence of Fourier coefficients, as is likely to be the case with sharply peaked or multimodal distributions. We suggest a way of overcoming these shortcomings.

摘要暂缺

正交级数密度估计中的项选择AI 解读

We show that Kronmal and Tarter's well-known rule for selecting the terms in an orthogonal series density estimator can lead to poor performance and even inconsistency in certain cases. These difficulties arise when the underlying density has a nonmonotone sequence of Fourier coefficients, as is likely to be the case with sharply peaked or multimodal distributions. We suggest a way of overcoming these shortcomings.

隐式统计模型推断的蒙特卡洛方法AI 解读

SUMMARY A prescribed statistical model is a parametric specification of the distribution of a random vector, whilst an implicit statistical model is one defined at a more fundamental level in terms of a generating stochastic mechanism. This paper develops methods of inference which can be used for implicit statistical models whose distribution theory is intractable. The kernel method of probability density estimation is advocated for estimating a log-likelihood from…

空间点过程的参数估计AI 解读

Summary In a recent paper, Ripley (1977) discusses the use of two goodness-of-fit criteria for spatial point pattern data. Both criteria can be adapted to the associated problem of parameter estimation. to illustrate their use in this respect, we re-work one of Ripley's examples and reach rather different conclusions.

空间点模式的稳健密度估计札记AI 解读

The author's earlier analytical results on the robustness of distance-based estimators of density are supplemented by a simulation study, with particular attention being given to various types of aggregated spatial point patterns. In addition, some remarks on heterogeneous patterns are made. Finally, the results of an application to data on tree locations are described.

空间点模式稳健密度估计札记AI 解读

The author's earlier analytical results on the robustness of distance-based estimators of density are supplemented by a simulation study, with particular attention being given to various types of aggregated spatial point patterns. In addition, some remarks on heterogeneous patterns are made. Finally, the results of an application to data on tree locations are described.

利用距离方法进行稳健密度估计AI 解读

Distance estimators of density may exhibit serious bias unless the population under consideration forms a completely random spatial pattern, i.e. the estimators are not robust. In this paper some new estimators are proposed, and their robustness is assessed analytically against two stochastic models, which together embrace a continuous range of spatial pattern, from extreme regularity, through randomness, to extreme aggregation.

基于距离方法的稳健空间密度估计AI 解读

Distance estimators of density may exhibit serious bias unless the population under consideration forms a completely random spatial pattern, i.e. the estimators are not robust. In this paper some new estimators are proposed, and their robustness is assessed analytically against two stochastic models, which together embrace a continuous range of spatial pattern, from extreme regularity, through randomness, to extreme aggregation.