Journal of the Royal Statistical Society: Series B

最新文章

• Model identification via total Frobenius norm of multivariate spectra AI翻译
  作者：McElroy, Tucker S.; Roy, Anindya
  摘要：We study the integral of the Frobenius norm as a measure of the discrepancy between two multivariate spectra. Such a measure can be used to fit time series models, and ensures proximity between model and process at all frequencies of the spectral density. We develop new asymptotic results for linear and quadratic functionals of the periodogram, and apply the integrated Frobenius norm to fit time series models and test whether model residuals are white noise. The case of structural time series ...
• Bayesian Pyramids: identifiable multilayer discrete latent structure models for discrete data AI翻译
  作者：Gu, Yuqi; Dunson, David B.
  作者单位：Columbia University; Duke University
  摘要：High-dimensional categorical data are routinely collected in biomedical and social sciences. It is of great importance to build interpretable parsimonious models that perform dimension reduction and uncover meaningful latent structures from such discrete data. Identifiability is a fundamental requirement for valid modeling and inference in such scenarios, yet is challenging to address when there are complex latent structures. In this article, we propose a class of identifiable multilayer (pote...
• A nested error regression model with high-dimensional parameter for small area estimation AI翻译
  作者：Lahiri, Partha; Salvati, Nicola
  作者单位：University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park; University of Pisa
  摘要：In this paper, we propose a flexible nested error regression small area model with high-dimensional parameter that incorporates heterogeneity in regression coefficients and variance components. We develop a new robust small area-specific estimating equations method that allows appropriate pooling of a large number of areas in estimating small area-specific model parameters. We propose a parametric bootstrap and jackknife method to estimate not only the mean squared errors but also other common...
• Kuldeep Kumar's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Kumar, Kuldeep
  作者单位：Bond University
• Vintage factor analysis with Varimax performs statistical inference AI翻译
  作者：Pilling, Mark
  作者单位：University of Cambridge
• Karl Rohe and Muzhe Zeng's reply to the Discussion of 'Vintage factor analysis with varimax performs statistical inference' AI翻译
  作者：Rohe, Karl; Zeng, Muzhe
  作者单位：University of Wisconsin System; University of Wisconsin Madison
• Quantile autoregressive conditional heteroscedasticity AI翻译
  作者：Zhu, Qianqian; Tan, Songhua; Zheng, Yao; Li, Guodong
  作者单位：Shanghai University of Finance & Economics; University of Connecticut; University of Hong Kong
  摘要：This article proposes a novel conditional heteroscedastic time series model by applying the framework of quantile regression processes to the ARCH(& INFIN;) form of the GARCH model. This model can provide varying structures for conditional quantiles of the time series across different quantile levels, while including the commonly used GARCH model as a special case. The strict stationarity of the model is discussed. For robustness against heavy-tailed distributions, a self-weighted quantile reg...
• Causal inference with invalid instruments: post-selection problems and a solution using searching and sampling AI翻译
  作者：Guo, Zijian
  作者单位：Rutgers University System; Rutgers University New Brunswick; Rutgers University System; Rutgers University New Brunswick
  摘要：Instrumental variable methods are among the most commonly used causal inference approaches to deal with unmeasured confounders in observational studies. The presence of invalid instruments is the primary concern for practical applications, and a fast-growing area of research is inference for the causal effect with possibly invalid instruments. This paper illustrates that the existing confidence intervals may undercover when the valid and invalid instruments are hard to separate in a data-depen...
• Causal inference on distribution functions AI翻译
  作者：Lin, Zhenhua; Kong, Dehan; Wang, Linbo
  作者单位：National University of Singapore; University of Toronto
  摘要：Understanding causal relationships is one of the most important goals of modern science. So far, the causal inference literature has focused almost exclusively on outcomes coming from the Euclidean space Rp. However, it is increasingly common that complex datasets are best summarized as data points in nonlinear spaces. In this paper, we present a novel framework of causal effects for outcomes from the Wasserstein space of cumulative distribution functions, which in contrast to the Euclidean sp...
• Christine P Chai's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng AI翻译
  作者：Chai, Christine P.
  作者单位：Microsoft