International Statistical Review最新文献

In biomedical research, often hierarchical binary and continuous responses need to be jointly modelled. In joint generalised linear mixed models, this can be done with correlated random effects, which allows examining the association structure between the various responses and the evolution of this association over time. In addition, the effect of covariates on all outcomes can be assessed simultaneously. Still, investigating this association is often limited to examining the correlations between the responses on an underlying scale. In addition, the interpretation of this hierarchical model is conditional on the subject-specific random effects. This paper extends this approach and shows how manifest correlations can be computed, that is, the associations between the observed responses. Further, a marginal model is formulated, in which the interpretation is no longer conditional on the random effects. In addition, prediction intervals are derived of one subvector of responses conditional on the other. These methods are applied in a case study of the lung function and allergic bronchopulmonary aspergillosis in patients with cystic fibrosis.

We describe models and likelihood-based estimation of the finite population mean for a survey subject to unit non-response, when post-stratification information is available from external sources. A feature of the models is that they do not require the assumption that the data are missing at random (MAR). As a result, the proposed models provide estimates under weaker assumptions than those required in the absence of post-stratification information, thus allowing more robust inferences. In particular, we describe models for estimation of the finite population mean of a survey outcome with categorical covariates and externally observed categorical post-stratifiers. We compare inferences from the proposed method with existing design-based estimators via simulations. We apply our methods to school-level data from California Department of Education to estimate the mean academic performance index (API) score in years 1999 and 2000. We end with a discussion.

The confluence of growing analytic capacities and global surveillance systems for seasonal infections has created new opportunities to further develop statistical methodology and advance the understanding of the global disease dynamics. We developed a framework to characterise the seasonality of infectious diseases for publicly available global health surveillance data. Specifically, we aimed to estimate the seasonal characteristics and their uncertainty using mixed effects models with harmonic components and the δ-method and develop multi-panel visualisations to present complex interplay of seasonal peaks across geographic locations. We compiled a set of 2 422 weekly time series of 14 reported outcomes for 173 Member States from the World Health Organization's (WHO) international influenza virological surveillance system, FluNet, from 02 January 1995 through 20 June 2021. We produced an analecta of data visualisations to describe global travelling waves of influenza while addressing issues of data completeness and credibility. Our results offer directions for further improvements in data collection, reporting, analysis and development of statistical methodology and predictive approaches.

The fused lasso signal approximator (FLSA) is a smoothing procedure for noisy observations that uses fused lasso penalty on unobserved mean levels to find sparse signal blocks. Several path algorithms have been developed to obtain the whole solution path of the FLSA. However, it is known that the FLSA has model selection inconsistency when the underlying signals have a stair-case block, where three consecutive signal blocks are either strictly increasing or decreasing. Modified path algorithms for the FLSA have been proposed to guarantee model selection consistency regardless of the stair-case block. In this paper, we provide a comprehensive review of the path algorithms for the FLSA and prove the properties of the recently modified path algorithms' hitting times. Specifically, we reinterpret the modified path algorithm as the path algorithm for local FLSA problems and reveal the condition that the hitting time for the fusion of the modified path algorithm is not monotone in a tuning parameter. To recover the monotonicity of the solution path, we propose a pathwise adaptive FLSA having monotonicity with similar performance as the modified solution path algorithm. Finally, we apply the proposed method to the number of daily-confirmed cases of COVID-19 in Korea to identify the change points of its spread.

The generalised regression estimator (GREG) uses auxiliary data that are available from the finite population to improve the efficiency of the estimator of a total (mean). Estimators of the variance of GREG that have been proposed in the sampling literature include those based on Taylor linearisation and the jackknife techniques. Approximations based on Taylor expansions are reasonable for large samples. However, when the sample size is small, the Taylor-based variance estimator has a large negative bias. The jackknife variance estimators overestimate the variance of GREG for small sample sizes. We offset these setbacks using a bootstrap procedure for estimating the variance of the GREG. The method uses a bootstrap population constructed with the model underlying the GREG estimator. Repeated samples are selected in the bootstrap population according to the design used to select the initial sample, and the variability associated with these bootstrap samples is used to compute the proposed bootstrap variance estimator. Simulations show that the new bootstrap estimator has a small bias for samples that have few observations.