臭氧技术在柴油机中的应用

通过对柴油发动机臭氧助燃机理分析,设计了臭氧发生器,并将其用于6135型柴油发动机台架试验中。试验结果证明：臭氧参与柴油发动机的燃烧过程起到了节油和降低排放的双重效果。     

An empirical investigation of human dyadic systems in the time and frequency domains

This paper presents time and frequency domain analyses of psychotherapy data drawn from a unique dyadic system--the patient/therapist (P/T) system. The data are constituted by a time series defined by which member of the P/T system held the speaker role at each second of ten recorded psychotherapy consultations. The series reflects a specific property of communication within the dyad. Each series significantly followed a first order autoregressive model. That is, each was highly sensitive to the prior state of the system, or equivalently, the system showed feedforward of its prior state. These models became more significant after intervention analysis further clarified the underlying autoregressive structure. The histograms of the frequency of the length of each utterance indicated an underlying Poisson model for the emergence of speech. The parameters of the Poisson and autoregressive models were used to determine blindly which of four pairs of interviews involved the same therapist with two different patients. The method correctly identified three of the therapist four pairs but failed to identify different interviews with the same patient, a significantly negative result. These results suggest that certain time domain measures are therapist-dominated, so that the therapist drives change in these areas within the P/T system more strongly than the patient. In the frequency domain, the coherency between each of the 45 pairs of speaker series was used in an attempt to identify which time series corresponded systematically to the same therapists or patients. This proved futile because no two pairs of series--no P/T system--showed significant non-zero coherency. This result suggests that the harmonics of the switching of the speaker role are not determined by the therapist or patient alone but by the P/T system as an entity. We conclude that human dyadic systems may show predominant influence of one member or jointly determined patterns that are highly distinctive.     

Slope Estimation for Bivariate Longitudinal Outcomes Adjusting for Informative Right Censoring Using Discrete Survival Model: Application to the Renal Transplant Cohort

Patients undergoing renal transplantation are prone to graft failure which causes lost of follow-up measures on their blood urea nitrogen and serum creatinine levels. These two outcomes are measured repeatedly over time to assess renal function following transplantation. Loss of follow-up on these bivariate measures results in informative right censoring, a common problem in longitudinal data that should be adjusted for so that valid estimates are obtained. In this study, we propose a bivariate model that jointly models these two longitudinal correlated outcomes and generates population and individual slopes adjusting for informative right censoring using a discrete survival approach. The proposed approach is applied to the clinical dataset of patients who had undergone renal transplantation. A simulation study validates the effectiveness of the approach.     

Studying the dependence between ordinal-nominal categorical variables via orthogonal polynomials

In situations where the structure of one of the variables of a contingency table is ordered recent theory involving the augmentation of singular vectors and orthogonal polynomials has shown to be applicable for performing symmetric and non-symmetric correspondence analysis. Such an approach has the advantage of allowing the user to identify the source of variation between the categories in terms of components that reflect linear, quadratic and higher-order trends. The purpose of this paper is to focus on the study of two asymmetrically related variables cross-classified to form a two-way contingency table where only one of the variables has an ordinal structure.     

Masking methods that preserve positivity constraints in microdata

Statistical agencies have conflicting obligations to protect confidential information provided by respondents to surveys or censuses and to make data available for research and planning activities. When the microdata themselves are to be released, in order to achieve these conflicting objectives, statistical agencies apply statistical disclosure limitation (SDL) methods to the data, such as noise addition, swapping or microaggregation. Some of these methods do not preserve important structure and constraints in the data, such as positivity of some attributes or inequality constraints between attributes. Failure to preserve constraints is not only problematic in terms of data utility, but also may increase disclosure risk.In this paper, we describe a method for SDL that preserves both positivity of attributes and the mean vector and covariance matrix of the original data. The basis of the method is to apply multiplicative noise with the proper, data-dependent covariance structure.     

A computational framework for empirical Bayes inference

In empirical Bayes inference one is typically interested in sampling from the posterior distribution of a parameter with a hyper-parameter set to its maximum likelihood estimate. This is often problematic particularly when the likelihood function of the hyper-parameter is not available in closed form and the posterior distribution is intractable. Previous works have dealt with this problem using a multi-step approach based on the EM algorithm and Markov Chain Monte Carlo (MCMC). We propose a framework based on recent developments in adaptive MCMC, where this problem is addressed more efficiently using a single Monte Carlo run. We discuss the convergence of the algorithm and its connection with the EM algorithm. We apply our algorithm to the Bayesian Lasso of Park and Casella (J. Am. Stat. Assoc. 103:681–686, 2008) and on the empirical Bayes variable selection of George and Foster (J. Am. Stat. Assoc. 87:731–747, 2000).     

AN EXACT CONDITIONAL TEST FOR COVARIANCE SELECTION MODELS

An exact conditional test is developed for testing the absence of an edge in a graphical covariance selection model and is shown to be equivalent to a test based on the partial correlation coefficient. An example is given.     

Improved likelihood-based inference for the stationary AR(2) model

An improved likelihood-based method based on Fraser et al. (1999) is proposed in this paper to test the significance of the second lag of the stationary AR(2) model. Compared with the test proposed by Fan and Yao (2003) and the signed log-likelihood ratio test, the proposed method has remarkable accuracy. Simulation studies are performed to illustrate the accuracy of the proposed method. Application of the proposed method on historical data is presented to demonstrate the implementation of this method. Furthermore, the method can be extended to the general AR(p) model.     

Preserving data utility via BART

When preparing data for public release, information organizations face the challenge of preserving the quality of data while protecting the confidentiality of both data subjects and sensitive data attributes. Without knowing what type of analyses will be conducted by data users, it is often hard to alter data without sacrificing data utility. In this paper, we propose a new approach to mitigate this difficulty, which entails using Bayesian additive regression trees (BART), in connection with existing methods for statistical disclosure limitation, to help preserve data utility while meeting confidentiality requirements. We illustrate the performance of our method through both simulation and a data example. The method works well when the targeted relationship underlying the original data is not weak, and the performance appears to be robust to the intensity of alteration.