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Backfitting in smoothing spline ANOVA

平滑样条 ANOVA 中的回拟合算法

A computational scheme for fitting smoothing spline ANOVA models to large data sets with a (near) tensor product design is proposed. Such data sets are common in spatial-temporal analyses. The proposed scheme uses the backfitting algorithm to take advantage of the tensor product design to save both computational memory and time. Several ways to further speed up the backfitting algorithm, such as collapsing component functions and successive over-relaxation, are discussed. An…

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A Composite Likelihood Approach to Binary Spatial Data

二元空间数据的复合似然方法

Conventional geostatistics addresses the problem of estimation and prediction for continuous observations. But in many practical applications in public health, environmental remediation, or ecological research the most commonly available data are in the form of counts (e.g., number of cases) or indicator variables denoting above or below threshold values. Also, in many situations it is less expensive to obtain an imprecise categorical observation than to obtain precise…

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Poisson/gamma random field models for spatial statistics

空间统计中的泊松/伽马随机场模型

Doubly stochastic Bayesian hierarchical models are introduced to account for uncertainty and spatial variation in the underlying intensity measure for point process models. Inhomogeneous gamma process random fields and, more generally, Markov random fields with infinitely divisible distributions are used to construct positively autocorrelated intensity measures for spatial Poisson point processes; these in turn are used to model the number and location of individual events.…

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Minimax estimation via wavelet shrinkage

通过小波收缩实现极小极大估计

We attempt to recover an unknown function from noisy, sampled data. Using orthonormal bases of compactly supported wavelets, we develop a nonlinear method which works in the wavelet domain by simple nonlinear shrinkage of the empirical wavelet coefficients. The shrinkage can be tuned to be nearly minimax over any member of a wide range of Triebel- and Besov-type smoothness constraints and asymptotically mini-max over Besov bodies with $p \leq q$. Linear estimates cannot…

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Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes

利用最近邻去除杂点以估计空间点过程特征

We consider the problem of detecting features in spatial point processes in the presence of substantial clutter. One example is the detection of minefields using reconnaissance aircraft images that identify many objects that are not mines. Our solution uses Kth nearest neighbor distances of points in the process to classify them as clutter or otherwise. The observed Kth nearest neighbor distances are modeled as a mixture distribution, the parameters of which are estimated by…

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Bayesian Inference on Network Traffic Using Link Count Data

利用链路计数数据进行网络流量的贝叶斯推断

We study Bayesian models and methods for analysing network traffic counts in problems of inference about the traffic intensity between directed pairs of origins and destinations in networks. This is a class of problems very recently discussed by Vardi in a 1996 JASA article and is of interest in both communication and transportation network studies. The current article develops the theoretical framework of variants of the origin-destination flow problem and introduces…

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Proportional odds regression and sieve maximum likelihood estimation

比例优势回归与筛最大似然估计

This paper introduces, under a censoring mechanism, an estimation procedure for the baseline function and the regression parameters in the proportional odds model based on the sieve maximum likelihood method. The procedure uses monotone splines of variable orders and knots as approximations for the baseline function and an approximately unbiased estimator of the comparative Kullback-Leibler risk to select the best approximation. The procedure adapts to various unknown…

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Smoothing Spline Models with Correlated Random Errors

具有相关随机误差的平滑样条模型

Spline-smoothing techniques are commonly used to estimate the mean function in a nonparametric regression model. Their performances depend greatly on the choice of smoothing parameters. Many methods of selecting smoothing parameters such as generalized maximum likelihood (GML), generalized cross-validation (GCV), and unbiased risk (UBR), have been developed under the assumption of independent observations. They tend to underestimate smoothing parameters when data are…

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Generalized Linear Models for Small-Area Estimation

用于小区域估计的广义线性模型

Bayesian methods have been used quite extensively in recent years for solving small-area estimation problems. Particularly effective in this regard has been the hierarchical or empirical Bayes approach, which is especially suitable for a systematic connection of local areas through models. However, the development to date has mainly concentrated on continuous-valued variates. Often the survey data are discrete or categorical, so that hierarchical or empirical Bayes…

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Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering

通过模型聚类识别含杂波的空间点过程特征

We consider the problem of detecting features, such as minefields or seismic faults, in spatial point processes when there is substantial clutter. We use model-based clustering based on a mixture model for the process, in which features are assumed to generate points according to highly linear multivariate normal densities, and the clutter arises according to a spatial Poisson process. Nonlinear features are represented by several densities, giving a piecewise linear…

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Signal Detection in Underwater Sound using Wavelets

利用小波检测水下声音信号

This article considers the use of wavelet methods in relation to a common signal processing problem, that of detecting transient features in sound recordings that contain interference or distortion. In this particular case, the data are various types of underwater sounds, and the objective is to detect intermittent departures (potential “signals”) from the background sound environment in the data (“noise”), where the latter may itself be evolving and changing over time. We…

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Modelling Longitudinal and Spatially Correlated Data: Methods, Applications, and Future Directions

Generalized Linear Models.- Linear Models, Vector Spaces, and Residual Likelihood.- An Assessment of Approximate Maximum Likelihood Estimators in Generalized Linear Models.- Scaled Link Functions for Heterogeneous Ordinal Response Data.- Longitudinal Data Analysis.- Software Design for Longitudinal Data Analysis.- Asymptotic Properties of Nonlinear Mized-Effects Models.- Structured Antedependence Models for Longitudinal Data.- Effect of Confounding and Other Misspecification…

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Smoothing Spline Models with Correlated Random Errors

具有相关随机误差的平滑样条模型

Spline-smoothing techniques are commonly used to estimate the mean function in a nonparametric regression model. Their performances depend greatly on the choice of smoothing parameters. Many methods of selecting smoothing parameters such as generalized maximum likelihood (GML), generalized cross-validation (GCV), and unbiased risk (UBR), have been developed under the assumption of independent observations. They tend to underestimate smoothing parameters when data are…

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Applications of Time Series Analysis in Astronomy and Meteorology

时间序列分析在天文学和气象学中的应用

Part 1 Time series - theory and methodology: A short history of time series, M.B. Priestley Frequency estimation, E.J. Hannan et al Asymptotic expansion of estimators for diffusions with small noises, N. Yoshida Spatial-temporal spectral analysis with random sampling, I-Shang Chow, Ken-Shin Lii Detection of periodicities, M.B. Priestley Spectral and multivariate probability density estimation of continuous time stationary processes from randomly sampled data, Elias Masry…

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The influence of sample size on the degree of redundancy in spatial lag operators

样本规模对空间滞后算子冗余程度的影响

This paper analyzes redundant paths in spatial lag operators. It is shown that the sample size and the order of spatial lag largely determine the occurrence rate of redundant paths in a spatial lag operator. A mathematical expression for the occurrence rate is derived. In a simulation study it is shown that the occurrence rate is a good measure for the severity of the error introduced in spatial models when one fails to eliminate redundant paths from the spatial lag operator.

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Nonparametric Maximum Likelihood Estimation of Features in Spatial Point Processes Using Voronoï Tessellation

利用 Voronoi 剖分非参数极大似然估计空间点过程特征

This article addresses the problem of estimating the support domain of a bounded point process in presence of background noise. This situation occurs, for example, in the detection of a minefield from aerial observations. A maximum likelihood estimator for a mixture of uniform point processes is derived using a natural partition of the space defined by the data themselves: the Voronoi tessellation. The methodology is tested on simulations and compared to a model-based…

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A Spatial Scan Statistic for Stochastic Scan Partitions

基于随机扫描分区的空间扫描统计量

This article develops a spatial scan statistic for homogeneity analysis of point processes that utilizes stochastic scan partitions. The derivation of the sampling distribution for the statistic yields an exact test. This test has the potential for improved power over conventional alternatives when the point process is embedded in an underlying continuous random field and is recommended in situations for which the location of subregions of nonhomogeneity in the point process…

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Bayesian Prediction of Transformed Gaussian Random Fields

变换高斯随机场的贝叶斯预测

A model for prediction in some types of non-Gaussian random fields is presented. It extends the work of Handcock and Stein to prediction in transformed Gaussian random fields, where the transformation is known to belong to a parametric family of monotone transformations. The Bayesian transformed Gaussian model (BTG) provides an alternative to trans-Gaussian kriging taking into account the major sources of uncertainty, including uncertainty about the “normalizing…

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Interdependent preferences: an econometric analysis

相互依赖偏好的计量分析

The theoretical model of Gaertner (1974) and Pollak (1976) for the interdependence of preferences in the Linear Expenditure System is estimated for a cross-section of households. The interdependence of consumption of different households has implications for the stochastic structure of the model and for the identifiability of its parameters. Both aspects are dealt with. The empirical results indicate a significant role played by the interdependence of preferences. One of its…

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Skewness for multivariate distributions: two approaches

This paper presents two approaches for qualitative, quantitative and comparative concepts of skewness to be defined with respect to the spatial median for multivariate distributions. They extend the known quantile-based notions defined for real distributions. The main tool for such extensions consists of a family of central parts that provide suitable generalizations of the real interquantile intervals.

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Subseries Methods in Regression

回归中的子序列方法

We extend subseries methods for stationary sequences to the regression setting. To estimate sampling distributions, the subseries approach computes the statistic of interest on all possible subseries of a shorter length than the original series, and uses the distribution of these replicates to mimic the distribution of the original statistic. For proper choice of subseries length, the regression parameters can be estimated to order O(n -2/3), thus improving on the normal…

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Analyses of Fish Species Richness with Spatial Covariate

包含空间协变量的鱼类物种丰富度分析

Legislation passed in 1990 reducing the allowable sulfur dioxide emission levels in the United States is expected to reduce acidity in the Adirondack region of New York State. The number of fish species in a lake (species richness) depends on a number of physical and chemical factors, including the area, elevation, and acidity of the lake. Data on these and other factors are available for 1,166 Adirondack lakes. The data are analyzed with the goal of quantifying the effects…

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