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ENTHIS LINK GOES TO A ENGLISH SECTIONFRTHIS LINK GOES TO A FRENCH SECTION The analysis of time series data with detection limits is challenging due to the high‐dimensional integral involved in the likelihood. Existing methods are either computationally demanding or rely on restrictive parametric distributional assumptions. We propose a semiparametric approach, where the temporal dependence is captured by parametric copula, while the marginal distribution is estimated non‐parametrically. Utilizing the properties of copulas, we develop a new copula‐based sequential sampling algorithm, which provides a convenient way to calculate the censored likelihood. Even without full parametric distributional assumptions, the proposed method still allows us to efficiently compute the conditional quantiles of the censored response at a future time point, and thus construct both point and interval predictions. We establish the asymptotic properties of the proposed pseudo maximum likelihood estimator, and demonstrate through simulation and the analysis of a water quality data that the proposed method is more flexible and leads to more accurate predictions than Gaussian‐based methods for non‐normal data. The Canadian Journal of Statistics 47: 438–454; 2019 © 2019 Statistical Society of Canada

This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST), Officeof Sponsored Research (OSR) under Award No. OSR-2019-CRG7-3800; by the IR/D program and grant DMS-1712760from the National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in thismaterial are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authorsthank Dr. Howard H. Chang for providing the satellite data.

INTRODUCTION: Penile cancer is a disease with high morbidity and mortality and is rare in developed countries. In the developing world, the incidence is significantly higher, and accounts for 1-2% of malignant disease in men. Penile cancer is associated with delayed diagnosis, often due to psychological factors. Web based resources are especially important when obtaining information from health professionals is challenging, such as when symptoms are embarrassing or stigmatised. OBJECTIVE: To assess the quality of information about penile cancer on the internet and to compare the quality of information from developed countries with developing countries. METHOD: Health on the Net (HON) principles were applied to websites using the Google search engine imbedded with HON toolbar. This was used to assess 750 websites in English, French, German, Spanish and Portuguese by two independent examiners using the key word 'penile cancer' in all languages. The first 150 websites in each language were analysed. Further analysis was completed comparing results between languages and site sponsors. RESULTS: Of the 750 websites analysed, 10.4% were HON accredited. There were significantly more HON accredited websites in English and French compared with Portuguese (P = 0.009 and P = 0.0007). A total of 45% of websites were sponsored by Commercial enterprise and 27% were sponsored by Government organisations. CONCLUSION: A lack of validation of penile cancer internet resources should be appreciated by clinicians. Additionally, there is a discrepancy in the quality of websites between languages, with significantly more resources available in the developed world. Limited available web resources in Spanish and Portuguese contribute to disparities in information access and disease outcomes.

Array-based CGH experiments are designed to detect genomic aberrations or regions of DNA copy-number variation that are associated with an outcome, typically a state of disease. Most of the existing statistical methods target on detecting DNA copy number variations in a single sample or array. We focus on the detection of group effect variation, through simultaneous study of multiple samples from multiple groups. Rather than using direct segmentation or smoothing techniques, as commonly seen in existing detection methods, we develop a sequential model selection procedure that is guided by a modified Bayesian information criterion. This approach improves detection accuracy by accumulatively utilizing information across contiguous clones, and has computational advantage over the existing popular detection methods. Our empirical investigation suggests that the performance of the proposed method is superior to that of the existing detection methods, in particular, in detecting small segments or separating neighboring segments with differential degrees of copy-number variation.

Summary The paper discusses the asymptotic validity of posterior inference of pseudo-Bayesian quantile regression methods with complete or censored data when an asymmetric Laplace likelihood is used. The asymmetric Laplace likelihood has a special place in the Bayesian quantile regression framework because the usual quantile regression estimator can be derived as the maximum likelihood estimator under such a model, and this working likelihood enables highly efficient Markov chain Monte Carlo algorithms for posterior sampling. However, it seems to be under-recognised that the stationary distribution for the resulting posterior does not provide valid posterior inference directly. We demonstrate that a simple adjustment to the covariance matrix of the posterior chain leads to asymptotically valid posterior inference. Our simulation results confirm that the posterior inference, when appropriately adjusted, is an attractive alternative to other asymptotic approximations in quantile regression, especially in the presence of censored data.

Length-biased time-to-event data commonly arise in epidemiological cohort studies and cross-sectional surveys. Ignoring length-biased sampling often leads to severe bias in estimating the survival time in the general population. We propose a flexible quantile regression framework for analysing the covariate effects on the population survival time under both length-biased sampling and random censoring. This framework allows for easy interpretation of the statistical model. Furthermore, it allows the covariates to have different impacts at different tails of the survival distribution and thus is able to capture important population heterogeneity. Using an unbiased estimating equation approach, we develop a new estimator that allows the censoring variable to depend on covariates in a non-parametric way. We establish the consistency and asymptotic normality for the proposed estimator. A lack-of-fit test is proposed for diagnosing the adequacy of the population quantile regression model. The finite sample performance of the proposed methods is assessed through a simulation study. We demonstrate that the proposed method is effective in discovering interesting covariate effects by analysing the Canadian Study of Health and Aging dementia data. Copyright © 2014 John Wiley & Sons Ltd

Anthraquinone (AQ)-based repellents have been shown to reduce Canada goose (Branta canadensis) use of turfgrass; however, impacts of frequent mowing on efficacy of AQ have not been studied. Our objective was to determine efficacy and longevity of a rain- fast AQ-based avian repellent, FlightControlH PLUS (FCP), as a deterrent of free-ranging resident Canada geese under 2 mowing frequencies. We conducted the study at 8 sites in the Triangle region (Raleigh, Durham, and Chapel Hill) of North Carolina, USA. We arranged our experiment in a randomized complete block design, with each of 8 sites containing 4 0.1-ha treatment combinations: 1) treated with FCP and mowed every 4 days (T4), 2) treated with FCP and mowed every 8 days (T8), 3) untreated and mowed every 4 days, and 4) untreated and mowed every 8 days. We conducted 4 37-day field sessions (Jun-Jul 2007, Sep-Oct 2007, Jun-Jul 2008, and Sep-Oct 2008), representing the summer molting phase and the full-plumage phase. Resident goose use (measured by daily no. of droppings) was 41-70% lower on treated plots than on untreated plots, but use was similar between T4 and T8. Average FCP coverage on grass blades decreased in coverage from approximately 95% to 10% over the 30-day posttreatment phase. Results indicate that resident Canada goose use of FCP-treated turfgrass areas was lower than untreated areas even when chemical coverage on grass was 10%. Further, mowing frequency did not have a clear impact on the efficacy of FCP as a Canada goose repellent.

Most existing methods for identifying aberrant regions with array CGH data are confined to a single target sample. Focusing on the comparison of multiple samples from two different groups, we develop a new penalized regression approach with a fused adaptive lasso penalty to accommodate the spatial dependence of the clones. The nonrandom aberrant genomic segments are determined by assessing the significance of the differences between neighboring clones and neighboring segments. The algorithm proposed in this article is a first attempt to simultaneously detect the common aberrant regions within each group, and the regions where the two groups differ in copy number changes. The simulation study suggests that the proposed procedure outperforms the commonly used single-sample aberration detection methods for segmentation in terms of both false positives and false negatives. To further assess the value of the proposed method, we analyze a data set from a study that identified the aberrant genomic regions associated with grade subgroups of breast cancer tumors.