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In this paper, we investigate the problem of testing nonparametric function in partial linear errors-in-variables models with response missing at random. In order to overcome the bias produced by measurement errors, two bias-corrected test statistics based on the quadratic conditional moment method are proposed. The limiting null distributions of the test statistics are established respectively and p values can be easily determined which show that the proposed test statistics have similar theoretical properties. Moreover, our tests can detect the alternatives distinct from the null hypothesis at the optimal nonparametric rate for local smoothing-based methods in this area. Simulation studies are conducted to demonstrate the performance of the proposed test methods and the proposed two tests give similar performances. A real data set from the ACTG 175 study is used for illustrating the proposed test methods.

In this article, we consider a partially linear panel data models with fixed effects. In order to accommodate the within-group correlation, we apply the block empirical likelihood procedure to partially linear panel data models with fixed effects, and prove a nonparametric version of Wilks’ theorem which can be used to construct the confidence region for the parametric. By the block empirical likelihood ratio function, the maximum empirical likelihood estimator of the parameter is defined and the asymptotic normality is shown. A simulation study and a real data application are undertaken to assess the finite sample performance of our proposed method.

Epigenetic factors are known to correlate with gene expression in the existing studies. However, quantitative models that accurately classify the highly and lowly expressed genes based on epigenetic factors are currently lacking. In this study, a new machine learning method combines histone modifications, DNA methylation, DNA accessibility, transcription factors, and trinucleotide composition with support vector machines (SVM) is developed in the context of human embryonic stem cell line (H1). The results indicate that the predictive accuracy will be markedly improved when the epigenetic features are considered. The predictive accuracy and Matthews correlation coefficient of the best model are as high as 95.96% and 0.92 for 10-fold cross-validation test, and 95.58% and 0.92 for independent dataset test, respectively. Our model provides a good way to judge a gene is either highly or lowly expressed gene by using genetic and epigenetic data, when the expression data of the gene is lacking. And a web-server GECES for our analysis method is established at http://202.207.14.87:8032/fuwu/GECES/index.asp, so that other scientists can easily get their desired results by our web-server, without going through the mathematical details.

Thermophilic proteins can thrive stalely at the high temperatures. Identification of thermophilic protein could be helpful to learn the function of protein. Automated prediction of thermophilic protein is an important tool for genome annotation. In this work, a powerful predictor is proposed by combining amino acid composition, evolutionary information, and acid dissociation constant. The overall prediction accuracy of 93.53% was obtained for using the algorithm of support vector machine. In order to check the performance of our method, two low-similarity independent testing datasets are used to test the proposed method. Comparisons with other methods show that the prediction results were better than other existing methods in literature. This indicates that our approach was effective to predict thermophilic proteins.

For the high-dimensional partially linear varying coefficient models where covariates in the nonparametric part are measured with additive errors, we, in this paper, study asymptotic distributions of a corrected empirical log-likelihood ratio function and maximum empirical likelihood estimator of the regression parameter. At the same time, based on penalized empirical likelihood (PEL) approach, the parameter estimation and variable selection of the model are investigated, the proposed PEL estimators are shown to possess the oracle property. Also, we introduce the PEL ratio statistic to test a linear hypothesis of the parameter and prove it follows an asymptotically chi-square distribution under the null hypothesis. Simulation study and real data analysis are undertaken to evaluate the finite sample performance of the proposed methods.

Multivariate response data often arise in practice and they are frequently subject to missingness. Under this circumstance, the standard sufficient dimension reduction (SDR) methods cannot be used directly. To reduce the dimension and estimate the central mean subspace, a profile least squares estimation method is proposed based on an inverse probability weighted technique. The profile least squares method does not need any distributional assumptions on the covariates and hence differs from existing SDR methods. The resulting estimator of the central mean subspace is proved to be asymptotically normal and root n consistent under some mild conditions. The structural dimension is determined by a BIC-type criterion and the consistency of its estimator is established. Comprehensive simulations and a real data analysis show that the proposed method works promisingly.

In this paper, we construct a nonparametric regression quantile estimator by using the local linear fitting for left-truncated data, and establish the Bahadur-type representation and asymptotic normality of the proposed estimator when the observations form a stationary α -mixing sequence. Finite-sample performance of the estimator is investigated via simulation studies.

Knowledge of the adaptation mechanism of enzymes to extreme pH values and distinguishing them from one another are necessary in the proteomics field, and would help in the drug design of stable enzymes. In this work, we have systematically analyzed the information of 105 acidic and 112 alkaline enzymes, and propose an approach for distinguishing acidic enzymes from alkaline enzymes by combining the amino acid composition, reduced amino acid composition, gene ontology, evolutionary information, and auto covariance of averaged chemical shift (acACS). The overall prediction accuracy is 94.01% by 10-fold cross-validation using the algorithm of support vector machine. This result is better than that obtained by other existing methods. The improvement of the overall prediction accuracy reaches up to 3.3% higher than those of the random forest algorithm and secondary structure amino acid composition. The acACS performance is excellent, indicating that our approach is better than other existing methods in the literature. A user-friendly web-server pred-enzymes for predicting acidic and alkaline enzymes has been established, which is accessible to the public.

This paper studies the partially time-varying coefficient models where some covariates are measured with additive errors. In order to overcome the bias of the usual profile least squares estimation when measurement errors are ignored, we propose a modified profile least squares estimator of the regression parameter and construct estimators of the nonlinear coefficient function and error variance. The proposed three estimators are proved to be asymptotically normal under mild conditions. In addition, we introduce the profile likelihood ratio test and then demonstrate that it follows an asymptotically χ 2 distribution under the null hypothesis. Finite sample behavior of the estimators is investigated via simulations too.

The effects of the additives of ethanol (EA) and methyl tert-butyl ether (MTBE) in various blend ratios into the gasoline fuel on the exhaust emissions and the catalytic conversion efficiencies were investigated in an EFI gasoline engine. The regulated exhaust emissions (CO, THC and NOX) and the unregulated exhaust emissions (benzene, formaldehyde, acetaldehyde, unburned EA and MTBE) before and after the three-way catalytic converter were measured. The experimental results showed that EA brought about generally lower regulated engine-out emissions than MTBE did. But, the comparison of the unregulated engine-out emissions between both additives was different. Concretely, the effect of EA on benzene emission was worse than that of MTBE on the whole, which was a contrast with formaldehyde emission. The difference in the acetaldehyde comparison depended much on the engine operating conditions, especially the engine speed. Both EA and MTBE were identified in the engine exhaust gases only when they were added to the fuel, and their volume fraction increased with blend ratios. The catalytic conversion efficiencies of the regulated emissions for the EA blends were in general lower than those for MTBE blends, especially at the low and high engine speeds. There was little difference in the catalytic conversion efficiencies for both benzene and formaldehyde, while distinct difference for acetaldehyde.

A diesel fuel additive has been synthesized from conversion of dimethyl ether (DME) using dielectric barrier discharge (DBD) plasma at atmospheric pressure and low temperatures. A high conversion of DME has been achieved. The product of such conversion is a mixture of hydrocarbons and oxygenates that can be used as high-performance diesel fuel additives. The maximum conversion of DME reached 47.2% with a selectivity of liquid product (a mixture of dimethoxy-containing hydrocarbons) more than 39.0% at 120°C and a 30 ml/min flow rate of DME.