论文检索

图数据与不规则多维数据的多尺度方法AI 解读

Summary For regularly spaced one-dimensional data, wavelet shrinkage has proven to be a compelling method for non-parametric function estimation. We create three new multiscale methods that provide wavelet-like transforms both for data arising on graphs and for irregularly spaced spatial data in more than one dimension. The concept of scale still exists within these transforms, but as a continuous quantity rather than dyadic levels. Further, we adapt recent empirical…

逆回归中的失拟检验及其生物光子成像应用AI 解读

Summary We propose two test statistics for use in inverse regression problems Y = Kθ+ε, where K is a given linear operator which cannot be continuously inverted. Thus, only noisy, indirect observations Y for the function θ are available. Both test statistics have a counterpart in classical hypothesis testing, where they are called the order selection test and the data-driven Neyman smooth test. We also introduce two model selection criteria which extend the classical Akaike…

含交互作用的半参数模型检验及基因—环境交互应用AI 解读

Summary Motivated from the problem of testing for genetic effects on complex traits in the presence of gene–environment interaction, we develop score tests in general semiparametric regression problems that involves Tukey style 1 degree-of-freedom form of interaction between parametrically and non-parametrically modelled covariates. We find that the score test in this type of model, as recently developed by Chatterjee and co-workers in the fully parametric setting, is biased…

DASSO：Dantzig 选择器与 Lasso 的联系AI 解读

Summary We propose a new algorithm, DASSO, for fitting the entire coefficient path of the Dantzig selector with a similar computational cost to the least angle regression algorithm that is used to compute the lasso. DASSO efficiently constructs a piecewise linear path through a sequential simplex-like algorithm, which is remarkably similar to the least angle regression algorithm. Comparison of the two algorithms sheds new light on the question of how the lasso and Dantzig…

超高维特征空间的确定性独立筛选AI 解读

Summary Variable selection plays an important role in high dimensional statistical modelling which nowadays appears in many areas and is key to various scientific discoveries. For problems of large scale or dimensionality p, accuracy of estimation and computational cost are two top concerns. Recently, Candes and Tao have proposed the Dantzig selector using L1-regularization and showed that it achieves the ideal risk up to a logarithmic factor log(p). Their innovative…

偏态数据的非参数异方差变换回归模型及医疗费用应用AI 解读

Summary We develop a new non-parametric heteroscedastic transformation regression model for predicting the expected value of the outcome of a patient with given patient's covariates when the distribution of the outcome is highly skewed with a heteroscedastic variance. In our model, we allow both the transformation function and the error distribution function to be unknown. We show that under some regularity conditions the estimators for regression parameters, the expected…

受控直接效应的估计AI 解读

Summary When regression models adjust for mediators on the causal path from exposure to outcome, the regression coefficient of exposure is commonly viewed as a measure of the direct exposure effect. This interpretation can be misleading, even with a randomly assigned exposure. This is because adjustment for post-exposure measurements introduces bias whenever their association with the outcome is confounded by more than just the exposure. By the same token, adjustment for…

贝叶斯假设检验中基于 Jeffreys 散度的先验推广AI 解读

Summary We introduce objective proper prior distributions for hypothesis testing and model selection based on measures of divergence between the competing models; we call them divergence-based (DB) priors. DB priors have simple forms and desirable properties like information (finite sample) consistency and are often similar to other existing proposals like intrinsic priors. Moreover, in normal linear model scenarios, they reproduce the Jeffreys–Zellner–Siow priors exactly.…

肥皂膜平滑AI 解读

Summary Conventional smoothing methods sometimes perform badly when used to smooth data over complex domains, by smoothing inappropriately across boundary features, such as peninsulas. Solutions to this smoothing problem tend to be computationally complex, and not to provide model smooth functions which are appropriate for incorporating as components of other models, such as generalized additive models or mixed additive models. We propose a class of smoothers that are…

澄清：回归模型选择的残差似然方法AI 解读

In regression models, Shi and Tsai (2002) applied the logarithm of the commonly used residual maximum likelihood function (2.2) to obtain the residual information criterion RIC in equation (2.10). Function (2.2) is the same as the modified profile likelihood function, but it is different from the complete residual maximum likelihood function that contains two additional terms, −12(n−k)log⁡(2π) and 12log⁡|X′X|⁠, on the right-hand side of expression (2.2). Because these two…

广义混合效应模型中的半参数推断AI 解读

Summary The paper presents a study of the generalized partially linear model including random effects in its linear part. We propose an estimator that combines likelihood approaches for mixed effects models, with kernel methods. Following the methodology of Härdle and co-workers, we introduce a test for the hypothesis of a parametric mixed effects model against the alternative of a semiparametric mixed effects model. The critical values are estimated by using a bootstrap…

利用替代数据改进半参数估计AI 解读

Summary The paper considers estimating a parameter β that defines an estimating function U(y, x, β) for an outcome variable y and its covariate x when the outcome is missing in some of the observations. We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The efficiency of existing estimators for β depends critically on correctly specifying the conditional expectation of U given the surrogate and the covariate.…

部分观测扩散过程的粒子滤波AI 解读

Summary We introduce a novel particle filter scheme for a class of partially observed multivariate diffusions. We consider a variety of observation schemes, including diffusion observed with error, observation of a subset of the components of the multivariate diffusion and arrival times of a Poisson process whose intensity is a known function of the diffusion (Cox process). Unlike currently available methods, our particle filters do not require approximations of the…

抽样偏差与 Logistic 模型AI 解读

Summary In a regression model, the joint distribution for each finite sample of units is determined by a function px(y) depending only on the list of covariate values x=(x(u1),…,x(un)) on the sampled units. No random sampling of units is involved. In biological work, random sampling is frequently unavoidable, in which case the joint distribution p(y,x) depends on the sampling scheme. Regression models can be used for the study of dependence provided that the conditional…

仅有汇总信息时聚类二元与计数数据的非参数推断AI 解读

Summary Data in the form of pairs (X,Y), where the response Y is a count, arise in many applications, including problems involving stratified or two-stage sampling. Such data are often analysed by using random-effects models, where the distribution of Y, conditional on X and on an unobserved random parameter Θ, is taken to be either binomial or Poisson, and the distribution of Θ is connected through a link function to a random effect. The latter is sometimes supposed to be…

基于稳健聚类的正态混合模型稳健估计AI 解读

Summary We introduce a robust estimation procedure that is based on the choice of a representative trimmed subsample through an initial robust clustering procedure, and subsequent improvements based on maximum likelihood. To obtain the initial trimming we resort to the trimmed k-means, a simple procedure designed for finding the core of the clusters under appropriate configurations. By handling the trimmed data as censored, maximum likelihood estimation provides in each step…

用潜在高斯过程建模稀疏广义纵向观测AI 解读

Summary In longitudinal data analysis one frequently encounters non-Gaussian data that are repeatedly collected for a sample of individuals over time. The repeated observations could be binomial, Poisson or of another discrete type or could be continuous. The timings of the repeated measurements are often sparse and irregular. We introduce a latent Gaussian process model for such data, establishing a connection to functional data analysis. The functional methods proposed are…

利用条件不相关分量建模多元波动率AI 解读

Summary We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix-valued processes. It is flexible in the sense that each CUC may be fitted separately with any appropriate univariate volatility model. Computationally it splits one high dimensional optimization problem into several lower dimensional subproblems. Consistency for the…

协方差图的各向同性谱加性模型AI 解读

Summary A class of additive covariance models of an isotropic random process is proposed, motivated by the spectral representation of the covariance function. Model parameters are estimated by using a special case of the minimum norm quadratic estimation estimator, whose asymptotic moments have convenient expressions in terms of spectral densities. Fitting a model in this class is equivalent to fitting an additive model of the spectral density. The class of spectral additive…

将随机效应模型置于生存分析框架AI 解读

Summary Logistic random-effects models are often employed in the analysis of correlated binary data. However, fitting these models is challenging, since the marginal distribution of the response variables is analytically intractable. Often, the random effects are treated as missing data for constructing traditional data augmentation algorithms. We create a novel alternative data augmentation scheme that simplifies the likelihood-based inference for logistic random-effects…

通过幂后验估计边际似然AI 解读

Summary Model choice plays an increasingly important role in statistics. From a Bayesian perspective a crucial goal is to compute the marginal likelihood of the data for a given model. However, this is typically a difficult task since it amounts to integrating over all model parameters. The aim of the paper is to illustrate how this may be achieved by using ideas from thermodynamic integration or path sampling. We show how the marginal likelihood can be computed via Markov…

基于小波或提升格式的缺失值插补方法AI 解读

Summary The paper proposes a new approach to imputation using the expected sparse representation of a surface in a wavelet or lifting scheme basis. Our method incorporates a Bayesian mixture prior for these wavelet coefficients into a Gibbs sampler to generate a complete posterior distribution for the variable of interest. Intuitively, the estimator operates by borrowing strength from those observed neighbouring values to impute at the unobserved sites. We demonstrate the…

由二元性状数据推断带年代的祖先树及其语言分化应用AI 解读

Summary Binary trait data record the presence or absence of distinguishing traits in individuals. We treat the problem of estimating ancestral trees with time depth from binary trait data. Simple analysis of such data is problematic. Each homology class of traits has a unique birth event on the tree, and the birth event of a trait that is visible at the leaves is biased towards the leaves. We propose a model-based analysis of such data and present a Markov chain Monte Carlo…

典型分析的半参数方法AI 解读

Summary Classical canonical correlation analysis is one of the fundamental tools in statistics to investigate the linear association between two sets of variables. We propose a method, called semiparametric canonical analysis, to generalize canonical correlation analysis to incorporate the important non-linear association. Semiparametric canonical analysis is easy to implement and interpret. Statistical properties are proved. A consistent estimation method is developed.…