Search

Statistical inference in the presence of nuisance functionals with complex survey data is an important topic in social and economic studies. The Gini index, Lorenz curves, and quantile shares are among the commonly encountered examples. The nuisance functionals are usually handled by a plug-in nonparametric estimator and the main inferential procedure can be carried out through a two-step generalized empirical likelihood method. Unfortunately, the resulting inference is not efficient and the nonparametric version of the Wilks' theorem breaks down even under simple random sampling. We propose an augmented estimating equations method with nuisance functionals and complex surveys. The second step augmented estimating functions obey the Neyman orthogonality condition and automatically handle the impact of the first step plug-in estimator, and the resulting estimator of the main parameters of interest is invariant to the first step method. More importantly, the generalized empirical likelihood-based Wilks' theorem holds for the main parameters of interest under the design-based framework for commonly used survey designs, and the maximum generalized empirical likelihood estimators achieve the semiparametric efficiency bound. Performances of the proposed methods are demonstrated through simulation studies and an application using the dataset from the New York City Social Indicators Survey. [ABSTRACT FROM AUTHOR]

This study aims to explore the potential aminopeptidases of Lysinibacillus sphaericus based on the unique metabolic characteristics of this species which cannot metabolize carbohydrates and may have a strong ability to metabolize amino acids. Fifteen peptidase-encoding genes predicted in L. sphaericus C3-41 have been heterologously expressed in Escherichia coli BL21, and of these genes, only Amp0279 shows a high ability to hydrolyze L-leucine-4-nitroanilide (Leu-pNA). Phylogenetic analysis, 3D-structure modeling, and enzyme assays indicated that Amp0279 should be a novel Co2+-dependent aminopeptidase belonging to the M29 family. The optimal conditions of Amp0279 were determined to be 50 °C and pH 8.0 with the addition of 100 μM Co2+, and under this condition, the specific activity of Amp0279 matched that of Flavourzyme® (3.54 × 104 vs. 3.37 × 104 U/mg for the protein ingredient of Flavourzyme®). Amp0279 is mainly expressed in the middle sporulation phase in wild-type L. sphaericus or in Bacillus subtilis under the control of the sporulation-dependent strong promoter pcry8E, which is carried by the recombinant vector pHT315-8E21b. Furthermore, the secretory expression systems based on B. subtilis and Corynebacterium glutamicum were used to enhance the soluble expression of Amp0279. Obvious expression and enzymatic activity were detected from the crude supernatant media of both host bacteria without further concentration and purification. Moreover, expression can occur in the vegetative phase in B. subtilis under the control of the Pgrac promoter. Key points: • A novel Co2+-dependent leucyl aminopeptidase Amp0279 originating from L. sphaericus was characterized. • The activity of Amp0279 as a leucyl aminopeptidase matches that of Flavourzyme® under optimal conditions. • B. subtilis– and C. glutamicum–based expression systems are built to promote secretory (soluble) Amp0279 expression. [ABSTRACT FROM AUTHOR]

Summary: We propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified estimating equation systems and is applicable to both smooth and non‐differentiable estimating functions. Our proposed Bayesian estimator is design consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically valid design‐based frequentist properties under single‐stage unequal probability sampling designs with small sampling fractions. Large sample properties of the Bayesian inference proposed are established for both non‐informative and informative priors under the design‐based framework. We also propose a Bayesian model selection procedure with complex survey data and show that it works for general sampling designs. An efficient Markov chain Monte Carlo procedure is described for the required computation of the posterior distribution for general vector parameters. Simulation studies and an application to a real survey data set are included to examine the finite sample performances of the methods proposed as well as the effect of different types of prior and different types of sampling design. [ABSTRACT FROM AUTHOR]

Summary: This paper provides an overview on two parallel approaches to design‐based inference with complex survey data: the pseudo empirical likelihood methods and the sample empirical likelihood methods. The general framework covers parameters defined through smooth or non‐differentiable estimating functions for analytic use of survey data as well as descriptive finite population parameters, and the theory focuses on point estimation, hypothesis tests and variable selection under an arbitrary sampling design. Major practical issues for the implementation of the methods, including computational algorithms, are briefly discussed. Results from simulation studies to compare the finite sample performances of the two approaches are presented. [ABSTRACT FROM AUTHOR]

Coefficient estimation in linear regression models with missing data is routinely done in the mean regression framework. However, the mean regression theory breaks down if the error variance is infinite. In addition, correct specification of the likelihood function for existing imputation approach is often challenging in practice, especially for skewed data. In this paper, we develop a novel composite quantile regression and a weighted quantile average estimation procedure for parameter estimation in linear regression models when some responses are missing at random. Instead of imputing the missing response by randomly drawing from its conditional distribution, we propose to impute both missing and observed responses by their estimated conditional quantiles given the observed data and to use the parametrically estimated propensity scores to weigh check functions that define a regression parameter. Both estimation procedures are resistant to heavy-tailed errors or outliers in the response and can achieve nice robustness and efficiency. Moreover, we propose adaptive penalization methods to simultaneously select significant variables and estimate unknown parameters. Asymptotic properties of the proposed estimators are carefully investigated. An efficient algorithm is developed for fast implementation of the proposed methodologies. We also discuss a model selection criterion, which is based on an IC Q -type statistic, to select the penalty parameters. The performance of the proposed methods is illustrated via simulated and real data sets.