时频混合重叠复用系统的快速检测算法

为提高通信系统的频谱效率,研究了一种时频混合重叠复用系统,并提出针对该系统的基于Gabor变换的快速检测算法和利用误差反向传播思想的改进检测算法。实验结果表明,与利用最大似然序列估计检测算法相比,该算法在牺牲部分性能的条件下,检测速度大幅提高,且系统性能和检测速度做了很好的折衷,在较高信噪比时仍然优于相同频谱效率的高阶调制。采用相互重叠的信号复用传输方式直接提高传输速率比使用同样频谱效率的高阶QAM调制能带来更大的增益。     

Principal point classification: Applications to differentiating drug and placebo responses in longitudinal studies

Principal points are cluster means for theoretical distributions. A discriminant methodology based on principal points is introduced. The principal point classification method is useful in clinical trials where the goal is to distinguish and differentiate between different treatment effects. Particularly, in psychiatric studies where placebo response rates can be very high, the principal point classification is illustrated to distinguish specific drug responders from non-specific placebo responders.     

Reliability models for categorical data

As assumed hypothetical consensus category corresponding to a case being classified provides a basis for assessment of reliability of judges. Equivalent judges are characterised by the joint probability distribution of the judge assignment and the consensus category. Estimates of the conditional probabilities of judge assignment given consensus category and of consensus category given judge assignments are indices of reliability. All parameters can be estimated if data include classifications of a number of cases by 3 or more judges. Restrictive assumptions are imposed to obtain models for data from classifications by two judges. Maximum likelihood estimation is discussed and illustrated by example for the 3 or more judges case.     

Approximate maximum likelihood estimation in the presence of extra-poisson variation

A two-stage hierarchical model for analysis of discrete data with extra-Poisson variation is examined. The model consists of a Poisson distribution with a mixing lognormal distribution for the mean. A method of approximate maximum likelihood estimation of the parameters is proposed. The method uses the EM algorithm and approximations to facilitate its implementation are derived. Approximate standard errors of the estimates are provided and a numerical example is used to illustrate the method.     

Estimating survival curves with left truncated and interval censored data via the ems algorithm

It is well-known that the nonparametric maximum likelihood estimator (NPMLE) of a survival function may severely underestimate the survival probabilities at very early times for left truncated data. This problem might be overcome by instead computing a smoothed nonparametric estimator (SNE) via the EMS algorithm. The close connection between the SNE and the maximum penalized likelihood estimator is also established. Extensive Monte Carlo simulations demonstrate the superior performance of the SNE over that of the NPMLE, in terms of either bias or variance, even for moderately large Samples. The methodology is illustrated with an application to the Massachusetts Health Care Panel Study dataset to estimate the probability of being functionally independent for non-poor male and female groups rcspectively.     

Estimation of parameters for a Birnbaum–Saunders regression model with censored data

Little work has been published on the analysis of censored data for the Birnbaum–Saunders distribution (BISA). In this article, we implement the EM algorithm to fit a regression model with censored data when the failure times follow the BISA. Three approaches to implement the E-Step of the EM algorithm are considered. In two of these implementations, the M-Step is attained by an iterative least-squares procedure. The algorithm is exemplified with a single explanatory variable in the model.     

Interval estimation for misclassification rate parameters in a complementary Poisson model

We investigate three interval estimators for binomial misclassification rates in a complementary Poisson model where the data are possibly misclassified: a Wald-based interval, a score-based interval, and an interval based on the profile log-likelihood statistic. We investigate the coverage and average width properties of these intervals via a simulation study. For small Poisson counts and small misclassification rates, the intervals can perform poorly in terms of coverage. The profile log-likelihood confidence interval (CI) is often proved to outperform the other intervals with good coverage and width properties. Lastly, we apply the CIs to a real data set involving traffic accident data that contain misclassified counts.     

Inference for the scale parameter of lifetime distribution of k-unit parallel system based on progressively censored data

In this paper, inference for the scale parameter of lifetime distribution of a k-unit parallel system is provided. Lifetime distribution of each unit of the system is assumed to be a member of a scale family of distributions. Maximum likelihood estimator (MLE) and confidence intervals for the scale parameter based on progressively Type-II censored sample are obtained. A β-expectation tolerance interval for the lifetime of the system is obtained. As a member of the scale family, half-logistic distribution is considered and the performance of the MLE, confidence intervals and tolerance intervals are studied using simulation.     

Extreme probabilities for contingency tables under row and column independence with application to fisher's exact test

Theorerms are proved for the maxima and minima of IIR_i!/IIC_j!/T!II_yij ! over r× c contingcncy tables Y=(y_ij) with row sums R₁,…,R_r, column sums C₁,…,C_c, and grand total T. These results are imlplemented into the network algorithm of Mehta and Patel (1983) for computing the P-value of Fisher's exact test for unordered r×c contingency tables. The decrease in the amount of computing time can be substantial when the column sums are very different.     

Simulation of the p-generalized Gaussian distribution

In this paper, we introduce the p-generalized polar methods for the simulation of the p-generalized Gaussian distribution. On the basis of geometric measure representations, the well-known Box–Muller method and the Marsaglia–Bray rejecting polar method for the simulation of the Gaussian distribution are generalized to simulate the p-generalized Gaussian distribution, which fits much more flexibly to data than the Gaussian distribution and has already been applied in various fields of modern sciences. To prove the correctness of the p-generalized polar methods, we give stochastic representations, and to demonstrate their adequacy, we perform a comparison of six simulation techniques w.r.t. the goodness of fit and the complexity. The competing methods include adapted general methods and another special method. Furthermore, we prove stochastic representations for all the adapted methods.