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Introduction: With COVID-19 vaccination rolled out globally, increasing numbers of studies have shown that booster vaccines can enhance an individual’s protection against the infection, hospitalization, and death caused by SARS-CoV-2. This study evaluated the effectiveness of COVID-19 vaccine BBIBP-CorV booster against being infected (susceptibility), infecting others (infectiousness), and spreading the disease from one to another (transmission). Methods: This retrospective cohort study investigated the close contacts of all officially ascertained COVID-19 confirmed cases in Urumqi, China between August 1 and September 7, 2022. Eligible records were divided into four subcohorts based on the vaccination status of both the close contact and their source case: group 2-2, 2-dose contacts seeded by 2-dose source case (as the reference level); group 2-3, 3-dose contacts seeded by 2-dose source case; group 3-2, 2-dose contacts seeded by 3-dose source case; and group 3-3, 3-dose contacts seeded by 3-dose source case. In the four subcohorts, multivariate logistic regression models were used to examine the vaccine effectiveness (VE) for the BBIBP-CorV booster dose. We adjusted for potential confounding variables, including the sex and age of source cases and close contacts, the calendar week of contact history and contact settings. We evaluated the statistical uncertainty using a 95% confidence interval (CI). In addition, we conducted subgroup analyses to evaluate VE by sex. Results: The sample sizes of groups 2-2, 2-3, 3-2, and 3-3 were 1184, 3773, 4723, and 27,136 individuals, respectively. Overall VE against susceptibility (group 2-3 vs 2-2) was 42.1% (95% CI 10.6, 62.5), VE against infectiousness (group 3-2 vs 2-2) was 62.0% (95% CI 37.2, 77.0), and VE against transmission (group 3-3 vs 2-2) was 83.7% (95% CI 75.1, 89.4). In the sex-stratified subgroups, male close contacts showed similar VE compared to the overall. However, among female close contacts, while the booster dose improved VE against infectiousness and VE against susceptibility, the VEs were not significantly different from zero. Conclusion: BBIBP-CorV vaccine booster was associated with mild to moderate levels of protection against Omicron susceptibility, infectiousness, and transmission. Real-world assessment of protective performance of COVID-19 vaccines against the risk of Omicron strains is continuously needed, and may provide information that helps vaccination strategy.

It’s well-known that change-point problem is an important part of model statistical analysis. Most of the existing methods are not robust to criteria of the evaluation of change-point problem. In this article, we consider “mean-shift” problem in change-point studies. A quantile test of single quantile is proposed based on saddlepoint approximation method. In order to utilize the information at different quantile of the sequence, we further construct a “composite quantile test” to calculate the probability of every location of the sequence to be a change-point. The location of change-point can be pinpointed rather than estimated within a interval. The proposed tests make no assumptions about the functional forms of the sequence distribution and work sensitively on both large and small size samples, the case of change-point in the tails, and multiple change-points situation. The good performances of the tests are confirmed by simulations and real data analysis. The saddlepoint approximation based distribution of the test statistic that is developed in the paper is of independent interest and appealing. This finding may be of independent interest to the readers in this research area.

An adaptive local smoothing method for nonparametric conditional quantile regression models is considered in this paper. Theoretical properties of the procedure are examined. The proposed method is fully adaptive in the sense that no prior information about the structure of the model is assumed. The fully adaptive feature not only allows varying bandwidths to accommodate jumps or instantaneous slope changes, but also allows the algorithm to be spatially adaptive. Under general conditions, precise risk bounds for homogeneous and heterogeneous cases of the underlying conditional quantile curves are established. An automatic selection algorithm for locally adaptive bandwidths is also given, which is applicable to higher dimensional cases. Simulation studies and data analysis confirm that the proposed methodology works well.

When dealing with regression analysis, heteroscedasticity is a problem that the authors have to face with. Especially if little information can be got in advance, detection of heteroscedasticity as well as estimation of statistical models could be even more difficult. To this end, this paper proposes a quantile difference method (QDM) that can effectively estimate the heteroscedastic function. This method, being completely free from the estimation of mean regression function, is simple, robust and easy to implement. Moreover, the QDM method enables the detection of heteroscedasticity without any restrictions on error terms, consequently being widely applied. What is worth mentioning is that based on the proposed approach estimators of both mean regression function and heteroscedastic function can be obtained. In the end, the authors conduct some simulations to examine the performance of the proposed methods and use a real data to make an illustration.

This article proposes a new lack-of-test based on the weighted ratio of residuals and variances for partially linear regression models. The large and small sampling properties of the proposed test are established. The testing procedure is illustrated via several examples. Simulation studies show that the testing procedures are powerful even in small samples. An application of the test to a real data set is presented.

Abstract: In this article we consider a sequence of hierarchical space model of inverse problems. The underlying function is estimated from indirect observations over a variety of error distributions including those that are heavy-tailed and may not even possess variances or means. The main contribution of this paper is that we establish some oracle inequalities for the inverse problems by using quantile coupling technique that gives a tight bound for the quantile coupling between an arbitrary sample p-quantile and a normal variable, and an automatic selection principle for the nonrandom filters. This leads to the data-driven choice of weights. We also give an algorithm for its implementation. The quantile coupling inequality developed in this paper is of independent interest, because it includes the median coupling inequality in literature as a special case.

Abstract: Risk difference (RD) has played an important role in a lot of biological and epidemiological investigations to compare the risks of developing certain disease or tumor for two drugs or treatments. When the disease is rare and acute, inverse sampling (rather than binomial sampling) is usually recommended to collect the binary outcomes. In this paper, we derive an asymptotic confidence interval estimator for RD based on the score statistic. To compare its performance with three existing confidence interval estimators, we employ Monte Carlo simulation to evaluate their coverage probabilities, expected confidence interval widths, and the mean difference of the coverage probabilities from the nominal confidence level. Our simulation results suggest that the score-test-based confidence interval estimator is generally more appealing than the Wald, uniformly minimum variance unbiased estimator and likelihood ratio confidence interval estimators for it maintains the coverage probability close to the desired confidence level and yields the shortest expected width in most cases. We illustrate these confidence interval construction methods with real data sets from a drug comparison study and a congenital heart disease study. [Copyright &y& Elsevier]

An effective data-analytic tool, sliced inverse regression(SIR), for the analysis of multivariate data was developed by Li (Technical Report, Department of Mathematics, UCLA, 1989) and Duan and Li (J. Amer. Statist. Assoc. 86 (1991) 316). It is a method for dimension reduction. Let (Y,X) be a (p+1)-dimensional random vector, with Y∈R1 and X∈Rp. Let Λ = E{Cov(X|Y)}. Since it is necessary for an estimate of Λ in the implementation of SIR, Li (1991) considered two-sliced estimate, Hsing and Carroll (Ann. Statist. Assoc. 20 (1992) 1042) derived the asymptotic properties for the estimate and Zhu and Ng (Statist. Sinica. 5 (1995) 727) discussed fixed-number-sliced estimate. In this paper, quasi-residuals-based estimate for Λ is proposed and asymptotic properties obtained. Sample properties are investigated in a simulation study. [Copyright &y& Elsevier]

Modern spatial temporal data are collected from sensor networks. Missing data problems are common for this kind of data. Making robust and accurate imputation is important in many applications. There are complex correlations in both spatial and temporal dimensions. Thus, it is even a challenge to model missing spatial-temporal data. In this article, the imputation of missing values is with the help of related covariates. First, we transform the original sensor × time observational matrix to a high order tensor by adding an extra temporal dimension. Then we integrate quantile tensor regression with tensor completion. The objective function includes check loss and nuclear norm penalty. An alternating update algorithm combined with alternating direction method of multipliers (ADMM) is developed to solve the objective function. Theoretical properties of the proposed estimator are investigated. Simulation studies show our proposed method is more robust and can get more accurate imputation results. Real data analysis about Beijing's PM 2.5 concentration level is conducted to verify the efficiency of the estimation procedure. [ABSTRACT FROM AUTHOR]

By incorporating the Expectation–maximization (EM) algorithm into composite asymmetric Laplace distribution (CALD), an iterative weighted least square estimator for the linear composite quantile regression (CQR) models is derived. Two selection methods for the number of composite quantiles via redefined AIC and BIC are developed. Finally, the proposed procedures are illustrated by some simulations. [ABSTRACT FROM AUTHOR]

The type-II progressively hybrid censoring scheme can be deemed as a mixture of type-II progressive and hybrid censoring schemes, which has been utilized to analyze lifetime data in the literature for exponential distribution and Weibull distribution and so on, where the experiment terminates at a pre-specified time. However, little attention has been paid to parametric estimation under this censoring scheme for the mixed exponential distribution (MED) model, which is an important model in life data analysis. Based on type-II progressively hybrid censored samples, the estimation problem of the MED is addressed. The closed form of maximum likelihood estimators (MLEs) of unknown parameters using the EM algorithm are obtained. Some Monte Carlo simulations are implemented and a real data set is analyzed to illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: A new estimation procedure based on the composite quantile regression is proposed for the semiparametric additive partial linear models, of which the nonparametric components are approximated by polynomial splines. The proposed estimation method can simultaneously estimate both the parametric regression coefficients and nonparametric components without any specification of the error distributions. The proposed estimation method is empirically shown to be much more efficient than the popular least-squares-based estimation method for non-normal random errors, especially for Cauchy error, and almost as efficient for normal random errors. To achieve sparsity in high-dimensional and sparse additive partial linear models, of which the number of linear covariates is much larger than the sample size but that of significant covariates is small relative to the sample size, a variable selection procedure based on adaptive Lasso is proposed to conduct estimation and variable selection simultaneously. The procedure is shown to possess the oracle property, and is much superior to the adaptive Lasso penalized least-squares-based method regardless of the random error distributions. In particular, two kinds of weights in the penalty are considered, namely the composite quantile regression estimates and Lasso penalized composite quantile regression estimates. Both types of weights perform very well with the latter performing especially well in terms of precisely selecting significant variables. The simulation results are consistent with the theoretical properties. A real data example is used to illustrate the application of the proposed methods. [Copyright &y& Elsevier]

This paper considers nonparametric quantile regression models for complex data of mixed categorical and continuous variables together with missing values at random (MAR). In consideration of the increasingly popular application of multiple imputation for handling missing data and the advantages of nonparametric quantile regression, we propose an effective and accurate multiple imputation method. The estimation procedure not only does well in modeling with mixed categorical and continuous data, but also makes full use of the entire data set to achieve increased efficiency. The proposed estimator is asymptotically normal. In simulation study, we compare the performance of the multiple imputation method with complete case (CC), Regression imputation and nearest-neighbor imputation methods, and outline advantages and drawbacks of the different methods. Simulation studies show that the proposed multiple imputation method performs better. [ABSTRACT FROM AUTHOR]

Nowadays, it has become increasingly common to store large-scale data sets distributedly across a great number of clients. The aim of the study is to develop a distributed estimator for generalized linear models (GLMs) in the "large n , diverging p n " framework with a weak assumption on the number of clients. When the dimension diverges at the rate of o (n) , the asymptotic efficiency of the global maximum likelihood estimator (MLE), the one-step MLE, and the aggregated estimating equation (AEE) estimator for GLMs are established. A novel distributed estimator is then proposed with two rounds of communication. It has the same asymptotic efficiency as the global MLE under p n = o (n). The assumption on the number of clients is more relaxed than that of the AEE estimator and the proposed method is thus more practical for real-world applications. Simulations and a case study demonstrate the satisfactory finite-sample performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

A new test statistic is proposed by utilizing the eigenvalues of the sample covariance matrix for the identity test. Under some general assumptions, asymptotic distributions of the proposed test statistic T and tests proposed in previous literature (denoted as T s , T 1 , T 2 ) are given. Simulations are also conducted to evaluate their performance in a finite sample. [ABSTRACT FROM AUTHOR]