Edge-fault-tolerant hamiltonicity of locally twisted cubes under conditional edge faults

The locally twisted cube is a variation of hypercube, which possesses some properties superior to the hypercube. In this paper, we investigate the edge-fault-tolerant hamiltonicity of an n-dimensional locally twisted cube, denoted by LTQ _n . We show that for any LTQ _n (n≥3) with at most 2n−5 faulty edges in which each node is incident to at least two fault-free edges, there exists a fault-free Hamiltonian cycle. We also demonstrate that our result is optimal with respect to the number of faulty edges tolerated.     

Modeling Heteroscedasticity in Daily Foreign-Exchange Rates

This article estimates autoregressive conditionally heteroscedastic (ARCH) and generalized ARCH (GARCH) models for five foreign currencies, using 10 years of daily data, a variety of ARCH and GARCH specifications, a number of nonnormal error densities, and a comprehensive set of diagnostic checks. It finds that ARCH and GARCH models can usually remove all heteroscedasticity in price changes in all five currencies. Goodness-of-fit diagnostics indicate that exponential GARCH with certain nonnormal distributions fits the Canadian dollar extremely well and the Swiss franc and the deutsche mark reasonably well. Only one nonnormal distribution fits the Japanese yen reasonably well. None fit the British pound.     

Risk perception and post-formation governance in international joint ventures in Taiwan: The perspective of the foreign partner

This paper draws on a risk analysis framework in order to develop a systematic understanding of the risks perceived by partners, and to investigate the implications of risk perception for the configuration of control, in the post-formation governance of international joint ventures. The key variables in this framework consist of six situational factors which are considered as potential antecedents of perceived risk: conflict, opportunism, cultural difference, dependence, partner fit, and ownership share; partner's perception of risk; and post-formation governance as a set of outcome variables. The framework is tested using a sample of international joint ventures located in Taiwan. The findings show that conflicts between partners, opportunistic behavior by the local partner, cultural differences, and perceived partner misfit are related to foreign partners' risk perceptions. This study suggests that when foreign partners face likely performance and partnership risks after an IJV is established, they tend to resort to tighter post-formation governance measures in order to increase or maintain their confidence in their joint ventures.     

Statistical modeling and inferences on dynamic decision-making mechanism underlying longitudinal distribution data with resource heterogeneity

We propose a new way of constructing dynamic decision-making model and a non-likelihood based statistical approach for analyzing a new data type: longitudinal distribution data. This longitudinal data records a trajectory of an animal’s dynamic decision-making when continuously exploiting a relative large, but close environment. The ensemble of all hosts contained in the environment is postulated to constitute a manifold of species-specific fitness-gains at any time point, and traverses through two major distinct phases: abundance vs. scarcity of pristine hosts. As such a manifold provides the relative potentials to all possible hosts available for selection, we construct a phase-dependent dynamic decision-making mechanism in a form of a self-adaptive conditional probabilistic model. We devise a Minimum Sum of Chi-squared approach to simultaneously evaluate individual cognitive capability within the two distinct phases and address the validity of the manifold based dynamic decision-making model on the longitudinal distribution data. We analyze three real data sets of seed beetle Callosobruchus maculatus collected from three experimental designs with different degrees of resource heterogeneity. Our statistical inferences are shown to successfully resolve the behavioral ecology issue of whether animal adaptively employs a dynamic decision-making mechanism in response to gradual environmental changes.     

Dating Trajectories From Middle to High School: Association With Academic Performance and Drug Use

This study identifies trajectories of dating from sixth to twelfth grade and describes the academic performance (teacher‐rated study skills and high school dropout) and self‐reported drug use associated with these trajectories, in a diverse sample randomly selected in sixth grade. Using a group‐based, semiparametric procedure, we identified four dating trajectories: low (16%), increasing (24%), high middle school (22%), and frequent (38%). Students in these latter two groups had significantly worse study skills, were four times more likely to drop out of school, and reported twice as much alcohol, tobacco, and marijuana use than students in the low and increasing dating groups. This study highlights the diversity of dating trajectories and some of the risks associated with early dating.     

Regression analysis based on conditional likelihood approach under semi-competing risks data

Medical studies often involve semi-competing risks data, which consist of two types of events, namely terminal event and non-terminal event. Because the non-terminal event may be dependently censored by the terminal event, it is not possible to make inference on the non-terminal event without extra assumptions. Therefore, this study assumes that the dependence structure on the non-terminal event and the terminal event follows a copula model, and lets the marginal regression models of the non-terminal event and the terminal event both follow time-varying effect models. This study uses a conditional likelihood approach to estimate the time-varying coefficient of the non-terminal event, and proves the large sample properties of the proposed estimator. Simulation studies show that the proposed estimator performs well. This study also uses the proposed method to analyze AIDS Clinical Trial Group (ACTG 320).     

How to Use Technical Terms in Ads? An FCB Grid Perspective

Research on the optimal use of technical terms in advertising can help sponsors effectively communicate complex technical concepts to consumers. This study therefore examined how technical terms, and the extent to which they are explained, influence advertising effectiveness for four product types in the FCB grid. The sample comprised 436 college students. The analytical results of this study indicate that using two technical terms together with detailed explanation of the terms improves consumer attitudes towards the ad and consumer evaluation of the advertised product for thinking (products meeting consumer utilitarian needs) and high involvement products. For thinking and low involvement products, using two technical terms together with detailed/rough explanation improves consumer attitudes towards the ad and consumer evaluation of the advertised product. Moreover, using two unexplained technical terms improves consumer attitudes towards the ad for feeling (those providing consumer gratification, social acceptance and sensory satisfaction) and high involvement products. Using no technical terms improves consumer attitudes towards the ad for feeling and low involvement products. Finally, theoretical and managerial implications and future research directions are discussed.     

The generalized inference on the sign testing problem about the normal variances

For the sign testing problem about the normal variances, we develop the heuristic testing procedure based on the concept of generalized test variable and generalized p-value. A detailed simulation study is conducted to empirically investigate the performance of the proposed method. Through the simulation study, especially in small sample sizes, the proposed test not only adequately controls empirical size at the nominal level, but also uniformly more powerful than likelihood ratio test, Gutmann's test, Li and Sinha's test and Liu and Chan's test, showing that the proposed method can be recommended in practice. The proposed method is illustrated with the published data.     

Generalized confidence interval estimation for the mean of delta-lognormal distribution: an application to New Zealand trawl survey data

Highly skewed and non-negative data can often be modeled by the delta-lognormal distribution in fisheries research. However, the coverage probabilities of extant interval estimation procedures are less satisfactory in small sample sizes and highly skewed data. We propose a heuristic method of estimating confidence intervals for the mean of the delta-lognormal distribution. This heuristic method is an estimation based on asymptotic generalized pivotal quantity to construct generalized confidence interval for the mean of the delta-lognormal distribution. Simulation results show that the proposed interval estimation procedure yields satisfactory coverage probabilities, expected interval lengths and reasonable relative biases. Finally, the proposed method is employed in red cod densities data for a demonstration.     

Measuring the Unmeasurable—A Study of Domestic Violence Risk Prediction and Management

ABSTRACT

The prevention of domestic violence (DV) have aroused serious concerns in Taiwan because of the disparity between the increasing amount of reported DV cases that doubled over the past decade and the scarcity of social workers. However, most common collaborations for DV prevention, between academic researchers and advocacy groups or governments outsourcing, often fail to produce effective prevention strategies. Hence, Data for Social Good Initiative (D4SG) worked with Taipei City Government to improve the efficiency of DV prevention and risk management on two levels—project collaboration level and data level. On the project collaboration level, we adopted a platform strategy and utilize public-private partnership (PPP) to connect and empower change agents across data silos from pilot runs to actual project execution. On the data level, we helped social workers differentiate the risk level of new cases by building a repeat victimization risk prediction model using random forest method with the 2015 data from Taipei City government’s DV database. The accuracy and F1-measure of our model were 96.3% and 62.8%. This projects’ PPP approach and quantification method successfully improved DV prevention process. These methodologies have also been applied to other work fields including firework prediction, emergency healthcare management as a paradigm.