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估计函数的窗口子抽样及其在回归模型中的应用AI 解读

We propose a subsampling method for estimating the asymptotic standard error of a statistic β n that is the solution to an estimating equation 1/n Σn j=1 Uj(Y j Xj, β) = 0 where the data Yj may be temporally or spatially correlated and the estimating function may depend on covariates Xj . A key statistic that we consider in detail is a generalized linear model regression coefficient computed under the assumption of independence. The availability of a consistent variance…

条件 Logistic 回归的高效蒙特卡洛方法AI 解读

Exact inference for the logistic regression model is based on generating the permutation distribution of the sufficient statistics for the regression parameters of interest conditional on the sufficient statistics for the remaining (nuisance) parameters. Despite the availability of fast numerical algorithms for the exact computations, there are numerous instances where a data set is too large to be analyzed by the exact methods, yet too sparse or unbalanced for the maximum…

马尔可夫链蒙特卡洛：用于贝叶斯推断的随机模拟AI 解读

Introduction Stochastic simulation Introduction Generation of Discrete Random Quantities Generation of Continuous Random Quantities Generation of Random Vectors and Matrices Resampling Methods Exercises Bayesian Inference Introduction Bayes' Theorem Conjugate Distributions Hierarchical Models Dynamic Models Spatial Models Model Comparison Exercises Approximate methods of inference Introduction Asymptotic Approximations Approximations by Gaussian Quadrature Monte Carlo…

随机过程的遍历性与稳定性AI 解读

Harris Markov chains ergodicity of non-Harris chains stochastic recursive sequences Markov chains in random environment multidimensional Markov processes two dimensional random walks three (and more) dimensional random walks continuous time Markov processes ergodicity of communication and queueing networks.

最大熵抽样与最优贝叶斯实验设计AI 解读

Summary When Shannon entropy is used as a criterion in the optimal design of experiments, advantage can be taken of the classical identity representing the joint entropy of parameters and observations as the sum of the marginal entropy of the observations and the preposterior conditional entropy of the parameters. Following previous work in which this idea was used in spatial sampling, the method is applied to standard parameterized Bayesian optimal experimental design.…

杂论：密度估计前的数据锐化AI 解读

We introduce a data-perturbation method for reducing bias of a wide variety of density estimators, in univariate, multivariate, spatial and spherical data settings. The method involves 'sharpening' the data by making them slightly more clustered than before, and then computing the estimator in the usual way, but from the sharpened data rather than the original data. The transformation depends in a simple, explicit way on the smoothing parameter employed for the density…

时空卡尔曼滤波的降维方法AI 解读

Many physical/biological processes involve variability over both space and time. As a result of difficulties caused by large datasets and the modelling of space, time and spatio-temporal interactions, traditional space-time methods are limited. In this paper, we present an approach to space-time prediction that achieves dimension reduction and uses a statistical model that is temporally dynamic and spatially descriptive. That is, it exploits the unidirectional flow of time,…

非可分时空平稳协方差函数类AI 解读

Suppose that a random process Z(s;t), indexed in space and time, has spatio-temporal stationary covariance C(h;u), where h ∈ ℝd (d ≥ 1) is a spatial lag and u ∈ ℝ is a temporal lag. Separable spatio-temporal covariances have the property that they can be written as a product of a purely spatial covariance and a purely temporal covariance. Their ease of definition is counterbalanced by the rather limited class of random processes to which they correspond. In this article we…

农业田间试验的贝叶斯分析AI 解读

Summary The paper describes Bayesian analysis for agricultural field experiments, a topic that has received very little previous attention, despite a vast frequentist literature. Adoption of the Bayesian paradigm simplifies the interpretation of the results, especially in ranking and selection. Also, complex formulations can be analysed with comparative ease, by using Markov chain Monte Carlo methods. A key ingredient in the approach is the need for spatial representations…

劳动供给实证模型中相互依赖行为的识别AI 解读

In this paper we test a particular form of interdependent behaviour, namely the hypothesis that individuals' choices of hours of work are influenced by the average hours of work in a social reference group. There are problems in empirically disentangling the effects of interdependent behaviour and preference variation across groups. We show that panel data or data from several points in time are needed. In the empirical analysis we combine cross-section data from 1973, 1980,…

利用金融与经济变量预测股票收益的非线性方法AI 解读

Inspired by the linear predictability and nonlinearity found in the finance literature, this article examines the nonlinear predictability of the excess returns. The relationship between the excess returns and the predicting variables is recursively modeled by a neural-network model, which is capable of performing flexible nonlinear functional approximation. The nonlinear neural-network model is found to have better in-sample fit and out-of-sample forecasts compared to its…

图像模型推断的成对似然方法AI 解读

We consider parameter estimation for certain random set models for image data. For the spatial models considered here, a full likelihood-based approach to this problem is often difficult, since the likelihood generally cannot be computed for images of typical size. This has motivated the consideration of various less computationally demanding parameter estimation techniques. We discuss one method based on likelihoods for small subsets of the data. Consideration of the basic…

摘要暂缺

利用平滑样条进行广义可加混合模型推断AI 解读

Summary Generalized additive mixed models are proposed for overdispersed and correlated data, which arise frequently in studies involving clustered, hierarchical and spatial designs. This class of models allows flexible functional dependence of an outcome variable on covariates by using nonparametric regression, while accounting for correlation between observations by using random effects. We estimate nonparametric functions by using smoothing splines and jointly estimate…

自适应小波估计：块阈值与oracle不等式方法AI 解读

We study wavelet function estimation via the approach of block thresholding and ideal adaptation with oracle. Oracle inequalities are derived and serve as guides for the selection of smoothing parameters. Based on an oracle inequality and motivated by the data compression and localization properties of wavelets, an adaptive wavelet estimator for nonparametric regression is proposed and the optimality of the procedure is investigated. We show that the estimator achieves…

面向地理学者的多元统计分析AI 解读

I. INTRODUCTION AND REVIEW. 1. Elementary Statistics Background. 2. Information Content in Geo-Referenced Data. 3. Introduction to Matrix Algebra. 4. Multiple Linear Regression Analysis and Correlation Analysis. II. INSTANCES OF THE GENERAL LINEAR MODEL. 5. Multivariate Analysis of Variance. 6. Principal Components and Factor Analysis. 7. Discriminant Function Analysis. 8. Cluster Analysis. 9. Canonical Correlation Analysis. III. NONLINEAR AND CATEGORICAL DATA MODELING. 10.…

重尾实用指南：统计技术与应用AI 解读

Part 1 Applications: heavy tailed probability distributions in the World Wide Web, M.E. Crovella et al self-similarity and heavy tails - structural modelling of network traffic, W. Willinger et al heavy tails in high-frequency financial data, U.A. Muller et al stable paretian modelling in finance - some empirical and theoretical aspects, S. Mittnik et al risk management and quantile estimation, F. Bassi et al. Part 2 Time series: analyzing stable time series, R.J. Adler et…

准线性小波估计AI 解读

The main paradigm of the modern wavelet theory of spatial adaptation formulated by Donoho and Johnstone is that there is a divergence between the linear minimax adaptation theory and the heuristic guiding algorithm development that leads to the necessity of using strongly nonlinear adaptive thresholded methods. On the other hand, it is well known that linear adaptive estimates are the best whenever an estimated function is smooth. Is it possible to suggest a quasi-linear…

利用子抽样预测空间累积分布函数AI 解读

The spatial cumulative distribution function (SCDF) is a random function that provides a statistical summary of a random field over a spatial domain of interest. In this article we develop a spatial subsampling method for predicting an SCDF based on observations made on a hexagonal grid, similar to the one used in the Environmental Monitoring and Assessment Program of the U.S. Environmental Protection Agency. We show that under quite general conditions, the proposed…

摘要暂缺

We study integration and reconstruction of Gaussian random functions with inhomogeneous local smoothness. A single realization may only be observed at a finite sampling design and the correct local smoothness is unknown. We construct adaptive two-stage designs that lead to asymptotically optimal methods. We show that every nonadaptive design is less efficient.

利用时空数据估计随机过程的积分AI 解读

Consider a space–time stochastic process $Z_ t(x)=S(x) + \xi_t(x)$ where $S(x)$ is a signal process defined on $\mathbb{R}^d$ and $\xi_t(x)$ represents measurement errors at time $t$. For a known measurable function $v(x)$ on $\mathbb{R}^d$ and a fixed cube $ D\subset \mathbb{R}^d$, this paper proposes a linear estimator for the stochastic integral $\int_D v(x)S(x)dx$ based on space–time observations $\{Z_t(x_i); i = 1,\ldots,n; t=1,\ldots,T\}$. Under mild conditions, the…

摘要暂缺

Many panel data sets encountered in macroeconomics, international economics, regional science, and finance are characterized by cross-sectional or "spatial" dependence. Standard techniques that fail to account for this dependence will result in inconsistently estimated standard errors. In this paper we present conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very…