-
作者：Goplerud, M.; Papaspiliopoulos, O.; Zanella, G.
作者单位：University of Texas System; University of Texas Austin; Bocconi University
摘要：While generalized linear mixed models are a fundamental tool in applied statistics, many specifications, such as those involving categorical factors with many levels or interaction terms, can be computationally challenging to estimate due to the need to compute or approximate high-dimensional integrals. Variational inference is a popular way to perform such computations, especially in the Bayesian context. However, naive use of such methods can provide unreliable uncertainty quantification. We...
-
作者：Mcgonigle, E. T.; Cho, H.
作者单位：University of Southampton; University of Bristol
摘要：Modern time series data often exhibit complex dependence and structural changes that are not easily characterized by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series. By considering joint characteristic functions between the time series and its lagged values, our proposed method is able to detect changepoints in the marginal distribution, but also those in possibly nonlinear serial dependence, all without the need to ...
-
作者：Tam, Edric; Dunson, David B.; Duan, Leo L.
作者单位：Stanford University; Duke University; State University System of Florida; University of Florida
摘要：Tree graphs are used routinely in statistics. When estimating a Bayesian model with a tree component, sampling the posterior remains a core difficulty. Existing Markov chain Monte Carlo methods tend to rely on local moves, often leading to poor mixing. A promising approach is to instead directly sample spanning trees on an auxiliary graph. Current spanning tree samplers, such as the celebrated Aldous-Broder algorithm, rely predominantly on simulating random walks that are required to visit all...
-
作者：Wiens, Douglas P.
作者单位：University of Alberta
摘要：We revisit a result according to which certain functions of covariance matrices are maximized at scalar multiples of the identity matrix. In a statistical context in which such functions measure loss, this says that the least favourable form of dependence is in fact independence, so that a procedure optimal for independent and identically distributed data can be minimax. In particular, the ordinary least squares estimate of a correctly specified regression response is minimax among generalized...
-
作者：Cinelli, Carlos; Hazlett, Chad
作者单位：University of Washington; University of Washington Seattle; University of California System; University of California Los Angeles
摘要：We develop an omitted variable bias framework for sensitivity analysis of instrumental variable estimates that naturally handles multiple side effects (violations of the exclusion restriction assumption) and confounders (violations of the ignorability of the instrument assumption) of the instrument, exploits expert knowledge to bound sensitivity parameters and can be easily implemented with standard software. Specifically, we introduce sensitivity statistics for routine reporting, such as (ext...
-
作者：Liang, Tengyuan; Recht, Benjamin
作者单位：University of Chicago; University of California System; University of California Berkeley
摘要：For decades, $ N $-of-1 experiments, where a unit serves as its own control and treatment in different time windows, have been used in certain medical contexts. However, due to effects that accumulate over long time windows and interventions that have complex evolution, a lack of robust inference tools has limited the widespread applicability of such $ N $-of-1 designs. This work combines techniques from experimental design in causal inference and system identification from control theory to p...
-
作者：Gilbert, Brian; Ogburn, Elizabeth L.; Datta, Abhirup
作者单位：Johns Hopkins University
摘要：This article addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under spatial confounding, the presence of an unmeasured spatially structured variable influencing both the exposure and the outcome. We first show that the estimators from ordinary least squares and restricted spatial regression are asymptotically biased under spatial confounding. We then prove a novel result on the infill consistency of the generalized ...
-
作者：Baddeley, A.; Davies, T. M.; Hazelton, M. L.
作者单位：Curtin University; University of Otago
摘要：The pair correlation function, or two-point correlation, of a spatial point process is a fundamental tool in spatial statistics and astrostatistics, measuring the strength of spatial dependence between points. Interest is focused on the behaviour of this function at short distances, but this is the region in which existing estimators can be particularly unreliable. We propose a new estimator of the pair correlation function based on techniques from stochastic geometry and kernel density estima...
-
作者：Palasciano, H. A.; Knight, M., I; Nason, G. P.
作者单位：Imperial College London; University of York - UK
摘要：This article introduces the class of continuous-time locally stationary wavelet processes. Continuous-time models enable us to properly provide scale-based time series models for irregularly spaced observations for the first time, while also permitting a spectral representation of the process over a continuous range of scales. We derive results for both the theoretical setting, where we assume access to the entire process sample path, and a more practical one, which develops methods for estima...
-
作者：Sjolander, A.; Hagg, S.
作者单位：Karolinska Institutet
摘要：A common aim of empirical research is to regress an outcome on a set of covariates, when some covariates are subject to missingness. If the probability of missingness is conditionally independent of the outcome, given the covariates, then a complete-case analysis is unbiased for parameters conditional on covariates. We derive all testable constraints that such outcome-independent missingness not at random implies on the observed data distribution, for settings where both the outcome and covari...
