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识别多元时间序列模型的统一方法AI 解读

This article proposes a Bayesian procedure for simultaneous identification of the Kronecker indices and model parameters of a multivariate linear system. The model parameters include the starting values and innovations of the system so that the series considered may be co-integrated or non-invertible. The procedure uses some recent developments in stochastic search variable selection in linear regression analysis and Markov chain Monte Carlo methods in statistical computing.…

现代回归方法AI 解读

This new edition of Regression Methods has been updated and enhanced to include all-new information on the latest advances and research in the evolving field of regression analysis. It provides a unique treatment of fundamental regression methods, such as diagnostics, transformations, robust regression, and ridge regression. Unifying key concepts and procedures, this new edition emphasizes applications to provide a more hands-on and comprehensive understanding of regression…

多元时间序列模型识别的统一方法AI 解读

This article proposes a Bayesian procedure for simultaneous identification of the Kronecker indices and model parameters of a multivariate linear system. The model parameters include the starting values and innovations of the system so that the series considered may be co-integrated or non-invertible. The procedure uses some recent developments in stochastic search variable selection in linear regression analysis and Markov chain Monte Carlo methods in statistical computing.…

机器学习与统计学：交汇AI 解读

Statistical Properties of Tree-Based Approaches to Classification The Decision Tree Algorithm CAL5 Based on a Statistical Approach to its Splitting Algorithm Probabilistic Symbolic Classifiers: An Empirical Comparison from a Statistical Perspective A Multistrategy Approach to Learning Multiple Dependent Concepts Quality of Decision Rules - Definition and Classification Schemes for Multiple Rules DIPOL - A Hybrid Piecewise Linear Classifier Combining Classification Procedures…

仅在备择下存在干扰参数的一致设定检验AI 解读

The nonparametric and the nuisance parameter approaches to consistently testing statistical models are both attempts to estimate topological measures of distance between a parametric and a nonparametric fit, and neither dominates in experiments. This topological unification allows us to greatly extend the nuisance parameter approach. How and why the nuisance parameter approach works and how it can be extended bear closely on recent developments in artificial neural networks.…

摘要暂缺

时间序列分析在天文学和气象学中的应用AI 解读

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…

弱依赖数据下的筛极值估计AI 解读

Many non/semiparametric time series estimates may be regarded as different forms of sieve extremum estimates. For stationary absolute regular mixing observations, the authors obtain convergence rates of sieve extremurn estimates and root-n asymptotic normality of 'plug-in' sieve extremum estimates of smooth functionals. As applications to time series models, they give convergence rates for nonparametric ARX(p,q) regression via neural networks, splines, wavelets; root-n…

局部平稳过程的自适应协方差估计AI 解读

It is shown that the covariance operator of a locally stationary process has approximate eigenvectors that are local cosine functions. We model locally stationary processes with pseudo-differential operators that are time-varying convolutions. An adaptive covariance estimation is calculated by searching first for a "best" local cosine basis which approximates the covariance by a band or a diagonal matrix. The estimation is obtained from regularized versions of the diagonal…

混合效应平滑样条方差分析AI 解读

Summary We propose a general family of nonparametric mixed effects models. Smoothing splines are used to model the fixed effects and are estimated by maximizing the penalized likelihood function. The random effects are generic and are modelled parametrically by assuming that the covariance function depends on a parsimonious set of parameters. These parameters and the smoothing parameter are estimated simultaneously by the generalized maximum likelihood method. We derive a…

二阶导数函数型回归及其近红外光谱应用AI 解读

Summary A linear regression method to predict a scalar from a discretized smooth function is presented. The method takes into account the functional nature of the predictors and the importance of the second derivative in spectroscopic applications. This motivates a functional inner product that can be used as a roughness penalty. Using this inner product, we derive a linear prediction method that is similar to ridge regression but with different shrinkage characteristics. We…

专利数据分析：混合泊松回归模型方法AI 解读

Count-data models are used to analyze the relationship between patents and research and development spending at the firm level, accounting for overdispersion using a finite mixed Poisson regression model with covariates in both Poisson rates and mixing probabilities. Maximum likelihood estimation using the EM and quasi-Newton algorithms is discussed. Monte Carlo studies suggest that (a) penalized likelihood criteria are a reliable basis for model selection and can be used to…

专利数据分析：混合泊松回归模型方法AI 解读

Count-data models are used to analyze the relationship between patents and research and development spending at the firm level, accounting for overdispersion using a finite mixed Poisson regression model with covariates in both Poisson rates and mixing probabilities. Maximum likelihood estimation using the EM and quasi-Newton algorithms is discussed. Monte Carlo studies suggest that (1) penalized likelihood criteria are a reliable basis for model selection and can be used to…

论筛法与惩罚法AI 解读

We develop a general theory which provides a unified treatment for the asymptotic normality and efficiency of the maximum likelihood estimates (MLE's) in parametric, semiparametric and nonparametric models. We find that the asymptotic behavior of substitution estimates for estimating smooth functionals are essentially governed by two indices: the degree of smoothness of the functional and the local size of the underlying parameter space. We show that when the local size of…

核不完全已知的反卷积及其在 FDG PET 血液曲线建模中的应用AI 解读

Camouflaged deconvolution arises when the kernel in a simple convolution model is not completely specified. We consider a situation in which the same fixed signal is repeatedly measured by separate convolutions with imprecisely known kernels. We develop a regularization methodology for application to these problems. The method involves simultaneous estimation of the target signal and the unknown parameters of the convolution kernels. Cross-validation is used to determine the…

卷积核未完全确定时的反卷积及其在 FDG PET 血液曲线建模中的应用AI 解读

Camouflaged deconvolution arises when the kernel in a simple convolution model is not completely specified. We consider a situation in which the same fixed signal is repeatedly measured by separate convolutions with imprecisely known kernels. We develop a regularization methodology for application to these problems. The method involves simultaneous estimation of the target signal and the unknown parameters of the convolution kernels. Cross-validation is used to determine the…

摘要暂缺

CART 与最佳正交基：一种联系AI 解读

We study what we call "dyadic CART" --a method of nonparametric regression which constructs a recursive partition by optimizing a complexity penalized sum of squares, where the optimization is over all recursive partitions arising from midpoint splits. We show that the method is adaptive to unknown degrees of anisotropic smoothness. Specifically, consider the anisotropic smoothness classes of Nikoľskii, consisting of bivariate functions $f(x_1, x_2)$ whose finite difference…

广义半线性模型的有效估计AI 解读

The generalized semilinear model (GSLIM) extends the generalized linear model by allowing the addition of an unknown smooth function to the predictor parameter, producing a semiparametric model. Some previously proposed score-based estimators for the parametric component in this model are not √n consistent. We propose efficient estimators for both the parametric and nonparametric components of the GSLIM. The estimator for the parametric component is the solution to an…

广义半线性模型的有效估计量AI 解读

The generalized semilinear model (GSLIM) extends the generalized linear model by allowing the addition of an unknown smooth function to the predictor parameter, producing a semiparametric model. Some previously proposed score-based estimators for the parametric component in this model are not √n consistent. We propose efficient estimators for both the parametric and nonparametric components of the GSLIM. The estimator for the parametric component is the solution to an…

部分线性模型中的惩罚准似然估计AI 解读

Consider a partial linear model, where the expectation of a random variable Y depends on covariates $(x, z)$ through $F(\theta_0 x + m_0(z))$, with $\theta_0$ an unknown parameter, and $m_0$ an unknown function. We apply the theory of empirical processes to derive the asymptotic properties of the penalized quasi-likelihood estimator.

人工神经网络的预测区间AI 解读

The artificial neural network (ANN) is becoming a very popular model for engineering and scientific applications. Inspired by brain architecture, artificial neural networks represent a class of nonlinear models capable of learning from data. Neural networks have been applied in many areas, including pattern matching, classification, prediction, and process control. This article focuses on the construction of prediction intervals. Previous statistical theory for constructing…

非参数回归中适应稀疏设计的插值方法AI 解读

We suggest interpolation methods for overcoming the problem of sparse design in local linear smoothing. These methods are based on simple rules, determined by the kernel and bandwidth, for deciding when and where pseudo-design points should be added to augment the original design sequence. New ordinates for the added design points are computed by simple interpolation, then local linear smoothing is applied directly to the expanded dataset. The method is competitive with…

毒性严重程度数据中的未观察到不良反应剂量水平AI 解读

Toxicity data are often categorized by severity of response and dose level with the assumption that there is a tolerated dose below which there is no toxicity. For data from a controlled experiment, the largest observed dose at or below the tolerated dose is called the no-observed-adverse-effect level (NOAEL). The problem of identifying the NOAEL can be viewed statistically as estimating the maximal observed dose for which there is no increased severity or frequency of toxic…