Prevalence of Sexual Dysfunction and Associated Risk Factors in Middle-Aged and Elderly Korean Men in Primary Care

Although several studies have individually investigated the risk factors for erectile dysfunction (ED), premature ejaculation (PE), and late-onset hypogonadism (LOH), few studies have considered ED, PE, and LOH as categories of sexual dysfunction (SD) within the same population. We therefore aimed to investigate the prevalence of SD and its associated risk factors among men in primary care. Study participants were enrolled by 18 family physicians from 15 hospital-based family practices in Korea between August 2010 and May 2011. Participants answered a questionnaire regarding their demographic characteristics and lifestyle factors as well as the Korean versions of the Androgen Deficiency in the Aging Male, the International Index of Erectile Function, and the Premature Ejaculation Diagnostic Tool questionnaires. SD prevalence was 64.9% among study participants who were ≥ 40 years of age. ED prevalence was 43.7%, PE prevalence was 38.6%, and LOH prevalence was 16.8%. SD prevalence was significantly associated with increased age, overweight, hypertension, diabetes, and depression. These findings highlight the importance of screening questions for SD in primary care, especially among older male patients with the identified risk factors.     

Perceived Ambiguity and Relevant Measures

We axiomatize preferences that can be represented by a monotonic aggregation of subjective expected utilities generated by a utility function and some set of i.i.d. probability measures over a product state space, S^∞. For such preferences, we define relevant measures, show that they are treated as if they were the only marginals possibly governing the state space, and connect them with the measures appearing in the aforementioned representation. These results allow us to interpret relevant measures as reflecting part of perceived ambiguity, meaning subjective uncertainty about probabilities over states. Under mild conditions, we show that increases or decreases in ambiguity aversion cannot affect the relevant measures. This property, necessary for the conclusion that these measures reflect only perceived ambiguity, distinguishes the set of relevant measures from the leading alternative in the literature. We apply our findings to a number of well‐known models of ambiguity‐sensitive preferences. For each model, we identify the set of relevant measures and the implications of comparative ambiguity aversion.     

Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models

Likelihood-based marginalized models using random effects have become popular for analyzing longitudinal categorical data. These models permit direct interpretation of marginal mean parameters and characterize the serial dependence of longitudinal outcomes using random effects [12,22]. In this paper, we propose model that expands the use of previous models to accommodate longitudinal nominal data. Random effects using a new covariance matrix with a Kronecker product composition are used to explain serial and categorical dependence. The Quasi-Newton algorithm is developed for estimation. These proposed methods are illustrated with a real data set and compared with other standard methods.     

Effect of finger interaction on individual finger: index finger

Grip strength is affected by such as enslaving effect, force sharing effect, and force deficit effect relating to the interaction among fingers. The author attempts to analyze index finger strength according to grip span of adjacent middle finger. 6 male graduate students participated as subjects in this experiment. The grip span has been applied from 45 mm to 60mm to investigate the effects of a coordination of grip spans on the index and middle finger strength. Results showed that index finger strength was the smallest in case of index finger grip span of 45 mm. Index finger strength decreased as the middle finger grip span. In general index finger strength shared a decreasing trend when the grip span of the middle finger increased. This study has an implication that the grip span of index and middle fingers influences force sharing effect on index finger.     

On the distributions of multivariate sample skewness

In this paper, we consider the multivariate normality test based on measure of multivariate sample skewness defined by Srivastava (1984). Srivastava derived asymptotic expectation up to the order N⁻¹ for the multivariate sample skewness and approximate χ²${\chi }^2$ test statistic, where N   is sample size. Under normality, we derive another expectation and variance for Srivastava's multivariate sample skewness in order to obtain a better test statistic. From this result, improved approximate χ²${\chi }^2$ test statistic using the multivariate sample skewness is also given for assessing multivariate normality. Finally, the numerical result by Monte Carlo simulation is shown in order to evaluate accuracy of the obtained expectation, variance and improved approximate χ²${\chi }^2$ test statistic. Furthermore, upper and lower percentiles of χ²${\chi }^2$ test statistic derived in this paper are compared with those of χ²${\chi }^2$ test statistic derived by Mardia (1974) which is used multivariate sample skewness defined by Mardia (1970).     

A note on some negative dependence notions

A random vector X = (X ₁,…,X _n ) is negatively associated if and only if for every pair of partitions X ₁ = (X _π(1),…,X _π(k)), X ₂ = (X _π(k+1),…,X _π(n)) of X , P( X ₁ ? A, X ₂ ? B) ≤ P( X ₁ ? A)P( X ₂ ? B) whenever A and B are open upper sets and π is any permutation of {1,…,n}. In this paper, we develop some of concepts of negative dependence, which are weaker than negative association but stronger than negative orthant dependence by requiring the above inequality to hold only for some upper sets A and B and applying the arguments in Shaked.     

Sociodemographic correlates of public perceptions regarding a smoking ban in bars and restaurants

As the health threat of environmental tobacco smoke is widely recognized, more state and local governments join the passage of ordinances that ban smoking in public establishments. This study investigated public perceptions regarding banning smoking in bars and restaurants among Indiana residents. A representative sample of 529 adult Indiana residents ages 18 or older was interviewed using random-digit dialing after two waves of pilot tests. Of the total respondents, 65% favored the smoking ban in bars and restaurants. Logistic regression analyses indicated that gender, education, and spouse's education were significant predictors for attitudes toward the smoking ban in bars and restaurants.     

Is There a Jump in the Transition?

This article develops a statistical test for the presence of a jump in an otherwise smooth transition process. In this testing, the null model is a threshold regression and the alternative model is a smooth transition model. We propose a quasi-Gaussian likelihood ratio statistic and provide its asymptotic distribution, which is defined as the maximum of a two parameter Gaussian process with a nonzero bias term. Asymptotic critical values can be tabulated and depend on the transition function employed. A simulation method to compute empirical critical values is also developed. Finite-sample performance of the test is assessed via Monte Carlo simulations. The test is applied to investigate the dynamics of racial segregation within cities across the United States.     

A new algorithm for maximum likelihood estimation in normal scale-mixture generalized autoregressive conditional heteroskedastic models

In this paper, we propose a new generalized autoregressive conditional heteroskedastic (GARCH) model using infinite normal scale-mixtures which can suitably avoid order selection problems in the application of finite normal scale-mixtures. We discuss its theoretical properties and develop a two-stage algorithm for the maximum likelihood estimator to estimate the mixing distribution non-parametric maximum likelihood estimator (NPMLE) as well as GARCH parameters (two-stage MLE). For the estimation of a mixing distribution, we employ a fast computational algorithm proposed by Wang [On fast computation of the non-parametric maximum likelihood estimate of a mixing distribution. J R Stat Soc Ser B. 2007;69:185–198] under the gradient characterization of the non-parametric mixture likelihood. The GARCH parameters are then estimated either using the expectation-mazimization algorithm or general optimization scheme. In addition, we propose a new forecasting algorithm of value-at-risk (VaR) using the two-stage MLE and the NPMLE. Through a simulation study and real data analysis, we compare the performance of the two-stage MLE with the existing ones including quasi-maximum likelihood estimator based on the standard normal density and the finite normal mixture quasi maximum estimated-likelihood estimator (cf. Lee S, Lee T. Inference for Box–Cox transformed threshold GARCH models with nuisance parameters. Scand J Stat. 2012;39:568–589) in terms of the relative efficiency and accuracy of VaR forecasting.