Markov chain Monte Carlo algorithms with sequential proposals

We explore a general framework in Markov chain Monte Carlo (MCMC) sampling where sequential proposals are tried as a candidate for the next state of the Markov chain. This sequential-proposal framework can be applied to various existing MCMC methods, including Metropolis–Hastings algorithms using random proposals and methods that use deterministic proposals such as Hamiltonian Monte Carlo (HMC) or the bouncy particle sampler. Sequential-proposal MCMC methods construct the same Markov chains as those constructed by the delayed rejection method under certain circumstances. In the context of HMC, the sequential-proposal approach has been proposed as extra chance generalized hybrid Monte Carlo (XCGHMC). We develop two novel methods in which the trajectories leading to proposals in HMC are automatically tuned to avoid doubling back, as in the No-U-Turn sampler (NUTS). The numerical efficiency of these new methods compare favorably to the NUTS. We additionally show that the sequential-proposal bouncy particle sampler enables the constructed Markov chain to pass through regions of low target density and thus facilitates better mixing of the chain when the target density is multimodal.

     

What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter

This study identifies perceived mobility, security, connectedness, system and service quality, usefulness, attitude, and flow experience as key motivational factors for using social networking services (SNSs), and develops a theoretical model that explicates the process in which users adopt Facebook and Twitter by integrating these factors with the technology acceptance model (TAM). While results of structural equation modeling (SEM) on the collected data (N = 2,214) verified the validity and reliability of the research model, Facebook and Twitter users were found to emphasize different motivational factors when deciding to use SNSs. The implications of notable findings and directions for future studies are discussed.     

Transportation Difficulty of Black and White Rural Older Adults

The purpose of this study was to understand self-reported transportation difficulty among rural older adults. We used data from the UAB Study of Aging (255 Black and 259 White), community-dwelling participants residing in rural areas. We examined the relationship of predisposing characteristics, enabling resources, and measures of need for care with self-reports of transportation difficulty. Blacks reported having more transportation difficulty than Whites (24.7% vs. 11.6%; p ≤ .05). When we introduced other variables, race differences disappeared, but there was a race by income interaction with transportation difficulty. Whites with lower incomes were more likely to have transportation difficulty than Whites with higher incomes. When data from Blacks and Whites were analyzed separately, income was the only variable associated with transportation difficulty among Whites. Among Blacks, income was not related to transportation difficulty but several variables other than income (age, gender, marital status, MMSE scores and depression) were.     

Political discontent in South Korea

This paper examines the nature and sources of political discontent in South Korea, one of the most successful third-wave democracies in East Asia. The analysis of a recent national sample survey indicates that ordinary people are able to distinguish among regime principles, regime performance, and regime institutions, which constitute separate targets of political discontent. The analysis also indicates that sources of political discontent vary depending on its targets. Noteworthy is that official corruption is most consistently related to disbelief in democratic principles, democratic dissatisfaction, and institutional distrust. Furthermore, less free and fair elections are related to more democratic dissatisfaction and institutional distrust. The results suggest that the democracy in Korea confronts not only critical citizens but also disloyal citizens suspicious of democracy. The fact that institutional trust declined, democratic satisfaction ceased to grow, the view of democracy as a universal value weakened while desire for democracy remained high suggests that the new democracy in Korea faces considerable difficulty, if not a crisis.     

The Cusum of Squares Test for Scale Changes in Infinite Order Moving Average Processes

In this paper we consider the problem of testing for a scale change in the infinite order moving average process X _j = _{i =0} a _{i j i} , where _j are i.i.d. r.v.s with E ₁ < for some > 0. In performing the test, a cusum of squares test statistic analogous to Inclan & Tiao's (1994) statistic is considered. It is well-known from the literature that outliers affect test procedures leading to false conclusions. In order to remedy this, a cusum of squares test based on trimmed observations is considered. It is demonstrated that this test is robust against outliers, is valid for infinite variance processes as well. Simulation results are given for illustration.     

Outlier detection using difference-based variance estimators in multiple regression

In this article, we propose an outlier detection approach in a multiple regression model using the properties of a difference-based variance estimator. This type of a difference-based variance estimator was originally used to estimate error variance in a non parametric regression model without estimating a non parametric function. This article first employed a difference-based error variance estimator to study the outlier detection problem in a multiple regression model. Our approach uses the leave-one-out type method based on difference-based error variance. The existing outlier detection approaches using the leave-one-out approach are highly affected by other outliers, while ours is not because our approach does not use the regression coefficient estimator. We compared our approach with several existing methods using a simulation study, suggesting the outperformance of our approach. The advantages of our approach are demonstrated using a real data application. Our approach can be extended to the non parametric regression model for outlier detection.     

How to build nomogram for type 2 diabetes using a naïve Bayesian classifier technique

In this study, we introduced a method for building a Bayesian nomogram and proposed an appropriate nomogram for type 2 diabetes (T2D) using data from 13,474 subjects collected from the 2013–2015 Korean National Health and Nutrition Examination Survey (KNHANES) data. We identified risk factors related to T2D, proposed a visual nomogram for T2D from a naïve Bayesian classifier model, and predicted incidence rates. Additionally, we computed confidence intervals for the influence of risk factors (attributes) and verified the proposed Bayesian nomogram using a receiver operating characteristic curve. Finally, we compared logistic regression and the Bayesian nomogram for T2D. The results of the analysis of the T2D data showed that the most influential factor among all attributes in the Bayesian nomogram was age group, and the highest risk factor for T2D incidence was cardiovascular disease. Dyslipidemia and hypertension also had significant impacts on T2D incidence while the effects of sex, smoking status, and employment status were relatively small compared to those of other variables. Using the proposed Bayesian nomogram, we can easily predict the incidence rate of T2D in an individual, and treatment plans can be established based on this information.