On the mean L1-error in the heteroscedastic deconvolution problem with compactly supported noises

We study the heteroscedastic deconvolution problem when random noises have compactly supported densities. In this context, the Fourier transforms of the densities can vanish on the real line. We propose a truncated type of estimator for target density and derive the convergence rate of the mean L¹-error uniformly over a class of target densities. A lower bound for the mean L¹-error is also established. Some simulations will be given to illustrate the performance of the proposed estimator.     

Relationship Between Socio-Economic Values and Wellbeing: An Overview Research in Asia

This paper investigates ten Asian nations to consider how socio-economic values affect happiness and satisfaction. Moreover, it considers whether economic factors can strongly affect wellbeing under certain conditions. Males in Asia are said they have more opportunities to obtain higher happiness and satisfaction but it does not happen in the current study. Unemployment has negative and significant impact with regard to happiness and satisfaction in developing countries yet it does not have the same effect in developed nations. It is believed cultural value positively affects happiness and satisfaction in East Asia but the result in the models is different with no clear relationship between this variable and wellbeing. Furthermore, some people declare that a relative increase of income compared to the lowest group will lead them to lower life satisfaction.     

Staying safe and visible via message sharing in online social networks

As an imperative channel for fast information propagation, online social networks (OSNs) also have their defects. One of them is the information leakage, i.e., information could be spread via OSNs to the users whom we are not willing to share with. Thus the problem of constructing a circle of trust to share information with as many friends as possible without further spreading it to unwanted targets has become a challenging research topic but still remained open. Our work is the first attempt to study the Maximum Circle of Trust problem seeking to share the information with the maximum expected number of poster’s friends such that the information spread to the unwanted targets is brought to its knees. First, we consider a special and more practical case with the two-hop information propagation and a single unwanted target. In this case, we show that this problem is NP-hard, which denies the existence of an exact polynomial-time algorithm. We thus propose a Fully Polynomial-Time Approximation Scheme (FPTAS), which can not only adjust any allowable performance error bound but also run in polynomial time with both the input size and allowed error. FPTAS is the best approximation solution one can ever wish for an NP-hard problem. We next consider the number of unwanted targets is bounded and prove that there does not exist an FPTAS in this case. Instead, we design a Polynomial-Time Approximation Scheme (PTAS) in which the allowable error can also be controlled. When the number of unwanted targets are not bounded, we provide a randomized algorithm, along with the analytical theoretical bound and inapproximaibility result. Finally, we consider a general case with many hops information propagation and further show its #P-hardness and propose an effective Iterative Circle of Trust Detection (ICTD) algorithm based on a novel greedy function. An extensive experiment on various real-world OSNs has validated the effectiveness of our proposed approximation and ICTD algorithms. Such an extensive experiment also highlights several important observations on information leakage which help to sharpen the security of OSNs in the future.     

Working Adults and Personal Business Ethics in South East Asia: a Comparative Study in Thailand and Vietnam

Global leaders and managers have been facing new challenges in the twenty-first century since globalization has created a much more integrated and borderless business environment. One of the key issues that they constantly deal with is business ethics. The global economy has made ethical issues become more complex and challenging. Businesses nowadays face urgent demands to act ethically and responsibly. In order for global leaders and managers to manage such a cultural diverse and complex workforce, they must have the ability to understand these complex issues and act ethically. They also need to possess adequate leadership skills to lead an ethical organization in a multinational environment. Thus, understanding and recognizing the cultural differences as well as the ethical standards of people in different countries are critical to the success of global leaders and managers. The purpose of this study is to investigate the perception of working professionals on business ethics in the two high-context cultures in South East Asia: Thailand and Vietnam. Using the Univariate Analysis of Variance method and adopting the widely-used Clark and Clark’s Personal Business Ethics Scores (PBES) measure, this study will compare the level of ethical maturity of the respondents based on a variety of variables including gender, business law course taken, code of conduct, ethics training, and government work experience. The authors will provide a thorough literature review on business ethics as well as the current ethical issues, i.e., bribery and corruption, in the two countries, together with practical suggestions and implications for educators, managers, and employees.     

Regenerative Markov Chain Monte Carlo for Any Distribution

While Markov chain Monte Carlo (MCMC) methods are frequently used for difficult calculations in a wide range of scientific disciplines, they suffer from a serious limitation: their samples are not independent and identically distributed. Consequently, estimates of expectations are biased if the initial value of the chain is not drawn from the target distribution. Regenerative simulation provides an elegant solution to this problem. In this article, we propose a simple regenerative MCMC algorithm to generate variates for any distribution.     

On the almost sure convergence for sums of negatively superadditive dependent random vectors in Hilbert spaces and its application

Abstract

This paper develops almost sure convergence for sums of negatively superadditive dependent random vectors in Hilbert spaces, we obtain Chung type SLLN and the Jaite type SLLN for sequences of negatively superadditive dependent random vectors in Hilbert spaces. Rate of convergence is studied through considering almost sure convergence to 0 of tail series. As an application, the almost sure convergence of degenerate von Mises-statistics is investigated.     

Stakeholder Green Pressure and New Product Performance in Emerging Countries: A Cross-country Study

Despite the burgeoning literature on stakeholder green pressure, research is scarce on how it influences eco-product innovation and new product performance. This article examines stakeholder green pressures as antecedents of eco-product innovation and new product performance in firms operating in resource-constrained countries. Using data gathered from surveys in Vietnam (N = 183) and Ghana (N = 217), we find that the positive effects of stakeholder green pressures on new product performance are serially mediated by environmental sustainability orientation and eco-product innovation. Our findings contribute to ongoing efforts to clarify the mechanisms channelling stakeholder pressures into new product performance in resource-constrained environments.     

A Hybrid Geometric Phase II/III Clinical Trial Design based on Treatment Failure Time and Toxicity

The problem of comparing several experimental treatments to a standard arises frequently in medical research. Various multi-stage randomized phase II/III designs have been proposed that select one or more promising experimental treatments and compare them to the standard while controlling overall Type I and Type II error rates. This paper addresses phase II/III settings where the joint goals are to increase the average time to treatment failure and control the probability of toxicity while accounting for patient heterogeneity. We are motivated by the desire to construct a feasible design for a trial of four chemotherapy combinations for treating a family of rare pediatric brain tumors. We present a hybrid two-stage design based on two-dimensional treatment effect parameters. A targeted parameter set is constructed from elicited parameter pairs considered to be equally desirable. Bayesian regression models for failure time and the probability of toxicity as functions of treatment and prognostic covariates are used to define two-dimensional covariate-adjusted treatment effect parameter sets. Decisions at each stage of the trial are based on the ratio of posterior probabilities of the alternative and null covariate-adjusted parameter sets. Design parameters are chosen to minimize expected sample size subject to frequentist error constraints. The design is illustrated by application to the brain tumor trial.