• Communications for Statistical Applications and Methods
• /
• v.6 no.1
• /
• pp.237-249
• /
• 1999

It has been issued that the irreconcilability of the classical test for a point null and standard Bayesian formulation for testing such a point null. The infimum of the posterior probability of the null hypothesis is used as measure of evidence against the null hypothesis in Bayesian approach; here the infimum is over the family of priors on the alternative hypotheses which includes all density that are a priori reasonable. For iid observations from a multivariate normal distribution in $\textit{p}$ dimensions with an unknown mean and a covariance matrix propotional to the Identity we consider the difference and the Wolfowitz distance of the distributions of the P-value and the lower bound of the posterior probability over the family of all normal priors. The Wolfowitz distance is interpreted as the average difference of the quantiles of the two distrbutions.

• Communications for Statistical Applications and Methods
• /
• v.11 no.1
• /
• pp.181-187
• /
• 2004

One of objectives in epidemiologic studies is to detect the amount of change caused by a specific risk factor. Risk ratio is one of the most useful measurements in epidemiology. When we perform the inference for this measurement with rare events, the standard approach based on the normal approximation may fail, in particular when there are no disease cases observed. In this paper, we discuss and evaluate several existing methods for constructing a confidence interval of risk ratio through simulation when the disease of interest is a rare event. The results in this paper provide guidance with how to construct interval estimates for risk difference and risk ratio when there are no disease cases observed.

• Communications for Statistical Applications and Methods
• /
• v.11 no.2
• /
• pp.399-411
• /
• 2004

A common method of handling nonresponse in sample survey is to delete the cases, which may result in a substantial loss of cases. Thus in certain situation, it is of interest to create a complete set of sample values. In this case, a popular approach is to impute the missing values in the sample by the mean or the median of responders. The difficulty with this method which just replaces each missing value with a single imputed value is that inferences based on the completed dataset underestimate the precision of the inferential procedure. Various suggestions have been made to overcome the difficulty but they might not be appropriate for public-use files where the user has only limited information for about the reasons for nonresponse. In this note, a multiple imputation method is considered to create complete dataset which might be used for all possible inferential procedures without misleading or underestimating the precision.

• Communications for Statistical Applications and Methods
• /
• v.23 no.4
• /
• pp.335-341
• /
• 2016

Interval censored failure time data often occurs in an observational study where a subject is followed periodically. Instead of observing an exact failure time, two inspection times that include it are made available. Several methods have been suggested to analyze interval censored failure time data (Sun, 2006). In this article, we are concerned with a binary time-varying covariate whose changing time is interval censored. A modified estimating equation is proposed by extending the approach suggested in the presence of a missing covariate. Based on simulation results, the proposed method shows a better performance than other simple imputation methods. ACTG 181 dataset were analyzed as a real example.

• Communications for Statistical Applications and Methods
• /
• v.23 no.4
• /
• pp.343-353
• /
• 2016

In doubly-censored data, an originating event time and a terminating event time are interval-censored. In certain analyses of such data, a researcher might be interested in the elapsed time between the originating and terminating events as well as regression modeling with risk factors. Therefore, in this study, we introduce a model evaluation method to measure the predictive ability of a model based on negative predictive values. We use a semiparametric estimate of the predictive accuracy to provide a simple and flexible method for model evaluation of doubly-censored survival outcomes. Additionally, we used simulation studies and tested data from a prostate cancer trial to illustrate the practical advantages of our approach. We believe that this method could be widely used to build prediction models or nomograms.

• Communications for Statistical Applications and Methods
• /
• v.23 no.4
• /
• pp.297-319
• /
• 2016

Football match predictions are of great interest to fans and sports press. In the last few years it has been the focus of several studies. In this paper, we propose the Poisson regression model in order to football match outcomes. We applied the proposed methodology to two national competitions: the 2012-2013 English Premier League and the 2015 Brazilian Football League. The number of goals scored by each team in a match is assumed to follow Poisson distribution, whose average reflects the strength of the attack, defense and the home team advantage. Inferences about all unknown quantities involved are made using a Bayesian approach. We calculate the probabilities of win, draw and loss for each match using a simulation procedure. Besides, also using simulation, the probability of a team qualifying for continental tournaments, being crowned champion or relegated to the second division is obtained.

• Communications for Statistical Applications and Methods
• /
• v.17 no.1
• /
• pp.17-27
• /
• 2010

Feature selection and feature weighting are useful techniques for improving the classification accuracy of k-Nearest Neighbor (k-NN) classifier. The main propose of feature selection and feature weighting is to reduce the number of features, by eliminating irrelevant and redundant features, while simultaneously maintaining or enhancing classification accuracy. In this paper, a novel hybrid approach is proposed for simultaneous feature selection, feature weighting and choice of k in k-NN classifier based on Genetic Algorithm. The results have indicated that the proposed algorithm is quite comparable with and superior to existing classifiers with or without feature selection and feature weighting capability.

• Journal of Environmental Health Sciences
• /
• v.28 no.3
• /
• pp.34-38
• /
• 2002

Statistical analysis between sewage plant operating parameters and the effluent quality was performed. We extracted two factors from principal component analysis of operating parameters and effluent quality from each plant. The total variance of 84.7%, 79.2% was explained by the two factors at SB plant and SC plant, respectively. The factors were identified at SB plant in the following order 1) the oxidation of organic material by aeration basin microbe,2) biomass in aeration basin and at SC plant 1) the oxidation of organic material by aeration basin microbe, 2) thickening of acti-vated sludge. These results suggested that the control of microbial composition might be critical on the improvement of the effluent quality and plant operating efficiency because most of the factors were related with microbes.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2008.11a
• /
• pp.821-824
• /
• 2008

This paper presents a systematic approach for estimating material performance of concrete mixture design based on satisfaction curves developed from statistical evaluation of existing or newly obtained material property related data. In performance based material design (PBMD) method, concrete material used for construction of a structure is designed considering a structure's specified performance requirements based on its usage and characteristics such as environmental conditions, structure types, expected design life, etc.Satisfaction curves express the probabilities that one component of substrates (i.e., aggregate size, cement content, etc) of concrete mixture will sustain different criterion value for a given concrete mixture design. This study presents a statistical analysis method for setting up concrete material parameter versus concrete criterion relationships in the form of satisfaction curves and for estimating confidence bounds on these satisfaction curves. This paper also presents an analysis method to combine multiple satisfaction curves to form one unique satisfaction curve that can relate the performance of concrete to a single evaluating value. Based on several evaluated mixture design examples for various material properties, the validity of the proposed method is discussed in detail.

• Journal of Communications and Networks
• /
• v.16 no.3
• /
• pp.264-269
• /
• 2014

We consider transmission over body area networks. Due to the difficulty in assessing an accurate statistical model valid for multiple scenarios, we advocate a system design technique favoring robustness. Our approach, which is based on results in [12] and generalizes them, examines the variation of a performance metric when the nominal statistical distribution of fading is replaced by the worst distribution within a given Kullback-Leibler divergence from it. The sensitivity of the performance metric to the divergence from the nominal distribution can be used as an indication of the design robustness. This concept is applied by evaluating the error probability of binary uncoded modulation and the outage probability-the first parameter is useful to assess system performance with no error-control coding, while the second reflects the performance when a near-optimal code is used. The usefulness of channel coding can be assessed by comparing its robustness with that of uncoded transmission.