2期

最新文章

• Sumca: simple, unified, Monte-Carlo-assisted approach to second-order unbiased mean-squared prediction error estimation AI翻译
  作者：Jiang, Jiming; Torabi, Mahmoud
  作者单位：University of California System; University of California Davis; University of Manitoba
  摘要：We propose a simple, unified, Monte-Carlo-assisted approach (called 'Sumca') to second-order unbiased estimation of the mean-squared prediction error (MSPE) of a small area predictor. The MSPE estimator proposed is easy to derive, has a simple expression and applies to a broad range of predictors that include the traditional empirical best linear unbiased predictor, empirical best predictor and post-model-selection empirical best linear unbiased predictor and empirical best predictor as specia...
• Doubly robust inference when combining probability and non-probability samples with high dimensional data AI翻译
  作者：Yang, Shu; Kim, Jae Kwang; Song, Rui
  作者单位：North Carolina State University; Iowa State University
  摘要：We consider integrating a non-probability sample with a probability sample which provides high dimensional representative covariate information of the target population. We propose a two-step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded concave penalties to select important variables and show selection consistency for general samples. In the second step, we focus on a doubly robust estimator of the finite ...
• Right singular vector projection graphs: fast high dimensional covariance matrix estimation under latent confounding AI翻译
  作者：Shah, Rajen D.; Frot, Benjamin; Thanei, Gian-Andrea; Meinshausen, Nicolai
  作者单位：University of Cambridge; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：We consider the problem of estimating a high dimensional pxp covariance matrix sigma, given n observations of confounded data with covariance sigma+Gamma Gamma T, where Gamma is an unknown pxq matrix of latent factor loadings. We propose a simple and scalable estimator based on the projection onto the right singular vectors of the observed data matrix, which we call right singular vector projection (RSVP). Our theoretical analysis of this method reveals that, in contrast with approaches based ...
• Exchangeable random measures for sparse and modular graphs with overlapping communities AI翻译
  作者：Todeschini, Adrien; Miscouridou, Xenia; Caron, Francois
  作者单位：Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Bordeaux; University of Oxford
  摘要：We propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process and naturally generalizes existing probabilistic models with overlapping block structure to the sparse regime. Our construction builds on vectors of completely random measures and has interpretable parameters, each node being assigned a vector representing its levels of affiliation to some latent communities. We develop met...
• Sparse principal component analysis via axis-aligned random projections AI翻译
  作者：Gataric, Milana; Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge
  摘要：We introduce a new method for sparse principal component analysis, based on the aggregation of eigenvector information from carefully selected axis-aligned random projections of the sample covariance matrix. Unlike most alternative approaches, our algorithm is non-iterative, so it is not vulnerable to a bad choice of initialization. We provide theoretical guarantees under which our principal subspace estimator can attain the minimax optimal rate of convergence in polynomial time. In addition, ...
• Semisupervised inference for explained variance in high dimensional linear regression and its applications AI翻译
  作者：Cai, T. Tony; Guo, Zijian
  作者单位：University of Pennsylvania; Rutgers University System; Rutgers University New Brunswick
  摘要：The paper considers statistical inference for the explained variance beta T sigma beta under the high dimensional linear model Y=X beta+epsilon in the semisupervised setting, where beta is the regression vector and sigma is the design covariance matrix. A calibrated estimator, which efficiently integrates both labelled and unlabelled data, is proposed. It is shown that the estimator achieves the minimax optimal rate of convergence in the general semisupervised framework. The optimality result ...
• Model misspecification in approximate Bayesian computation: consequences and diagnostics AI翻译
  作者：Frazier, David T.; Robert, Christian P.; Rousseau, Judith
  作者单位：Monash University; Universite PSL; Universite Paris-Dauphine; University of Warwick; University of Oxford
  摘要：We analyse the behaviour of approximate Bayesian computation (ABC) when the model generating the simulated data differs from the actual data-generating process, i.e. when the data simulator in ABC is misspecified. We demonstrate both theoretically and in simple, but practically relevant, examples that when the model is misspecified different versions of ABC can yield substantially different results. Our theoretical results demonstrate that even though the model is misspecified, under regularit...
• Functional models for time-varying random objects AI翻译
  作者：Dubey, Paromita; Mueller, Hans-Georg
  作者单位：University of California System; University of California Davis
  摘要：Functional data analysis provides a popular toolbox of functional models for the analysis of samples of random functions that are real valued. In recent years, samples of time-varying object data such as time-varying networks that are not in a vector space have been increasingly collected. These data can be viewed as elements of a general metric space that lacks local or global linear structure and therefore common approaches that have been used with great success for the analysis of functiona...
• Multiply robust causal inference with double-negative control adjustment for categorical unmeasured confounding AI翻译
  作者：Shi, Xu; Miao, Wang; Nelson, Jennifer C.; Tchetgen Tchetgen, Eric J.
  作者单位：University of Michigan System; University of Michigan; Peking University; Kaiser Permanente; University of Pennsylvania
  摘要：Unmeasured confounding is a threat to causal inference in observational studies. In recent years, the use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a long-standing tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao and colleagues have described sufficient conditions under which a pair of negative con...