Powerful Test of Two Proportions by Assuming a Registered Prior Density

A method for constructing powerful significance tests for the equivalence of two proportions is proposed by assuming prior density values. Recent changes in the medical research environment emphasize the need for choice of a prior density in advance of any study. The proposed test is based on the posterior probability of the alternative model and preserves the significance level with minimal reduction of power. The new test performs better than the familiar mid-p test under the uniform prior density condition. In addition, the computational burden is low. Potential extensions of the proposed test to related problems are also discussed.     

Sampling Some Truncated Distributions Via Rejection Algorithms

In this article, we develop rejection sampling algorithms to sample from some truncated and tail distributions. Such samplers are needed in many Markov chain Monte Carlo methods, often in connection with Bayesian inference. In addition to univariate normal, gamma, and beta distributions, we consider multivariate normal distributions truncated to certain sets.     

Sample size determination for testing equality in Poisson frequency data under an AB/BA crossover trial

Assuming that the frequency of occurrence follows the Poisson distribution, we develop sample size calculation procedures for testing equality based on an exact test procedure and an asymptotic test procedure under an AB/BA crossover design. We employ Monte Carlo simulation to demonstrate the use of these sample size formulae and evaluate the accuracy of sample size calculation formula derived from the asymptotic test procedure with respect to power in a variety of situations. We note that when both the relative treatment effect of interest and the underlying intraclass correlation between frequencies within patients are large, the sample size calculation based on the asymptotic test procedure can lose accuracy. In this case, the sample size calculation procedure based on the exact test is recommended. On the other hand, if the relative treatment effect of interest is small, the minimum required number of patients per group will be large, and the asymptotic test procedure will be valid for use. In this case, we may consider use of the sample size calculation formula derived from the asymptotic test procedure to reduce the number of patients needed for the exact test procedure. We include an example regarding a double‐blind randomized crossover trial comparing salmeterol with a placebo in exacerbations of asthma to illustrate the practical use of these sample size formulae. Copyright © 2013 John Wiley & Sons, Ltd.     

Exact and approximate distributions of the chi-square statistic for equiprobability

The distribution of the chi-square goodness-of-fit statistic is studied in the equiprobable case. Tables of exact critical values are given for a = .1, .05, .01, .005; k = 2(1)4, N = 26(1)50; k = 5, N = 26(1)40; k = 6(1)10, N = 26(1)30, where a is the desired significance level, k is the number of cells and N is the sample size. Methods of fitting the true distribution are compared. If k> 3, it is found that a simple additive adjustment to the asymptotic chi-square fit leads to high accuracy even for N between 10 and 20. For k = 2, the Yates corrected chi-square statistic is very accurately fitted by the usual chi-square distribution.     

Untransformed first observation problem in regression model with moving average process

This paper shows the impact of underestimation of variance of an estima-tor when first observation is left untransformed to simplify the computational procedure. In fact, the bias of the variance is not diminishing even for large sample size for the model considered. By partitioning the covariance matrix into two parts, this paper explains why least square estimator with untrans-formed first observation shows such a consequence. To demonstrate this, an exact GLS estimator is developed by modifying an approximate estimator. Nonetheless, the computational simplicity remains same.     

授权链路保护的频谱分配算法

研究了在保护授权链路的前提下认知无线电网络(CRN)的频谱优化分配问题。基于博弈论提出了一种新型的频谱分配模型,设计了一种保护授权链路的快速收敛的频谱分配算法(FCAPLL)。仿真分析证明,FCAPLL算法能在保护授权链路的前提下对认知无线电(CR)链路进行最优化频谱分配,有较快的收敛速度;同时仿真给出了网络性能参数与比例因子的关系,为FCAPLL应用于不同性能要求的CRN提供了依据。     

Maximum likelihood estimation for a nearly random walk model

This paper proposes an effective reparameterization method for the maximum likelihood estimation of a nearly random walk ARIMA (1,1,1) model, an important case where standard method of locating the MLE is not satisfactory. This model is equivalent to the permanent and temporary components model that Fama &French (1988) and others used to capture the slow mean reversion behavior of stock prices. The reparameterization method we prppose for estimating the nearly cancelled AR and MA parameters performs satisfactorily. The exact likelihood function based on the transformed parameters is studied. We argue that the region of interest will get magnified and emphasized in the transformed space, thus making the search for MLE more thorough and effective. Substantiai simuiation evidences are provided to demonstrate the effectiveness of the method. The sample size requirement is critical and is discussed in details. For application, this method is applied to estimate a nearly random walk ARIMA (1,1,1) model for NYSE/AMEX value-weighted market return in daily and longer holding-period horizons.     

On Consistency of the Self-Consistent Estimator of Survival Functions with Interval-Censored Data

The self-consistent estimator is commonly used for estimating a survival function with interval-censored data. Recent studies on interval censoring have focused on case 2 interval censoring, which does not involve exact observations, and double censoring, which involves only exact, right-censored or left-censored observations. In this paper, we consider an interval censoring scheme that involves exact, left-censored, right-censored and strictly interval-censored observations. Under this censoring scheme, we prove that the self-consistent estimator is strongly consistent under certain regularity conditions.