Efficient and flexible model-based clustering of jumps in diffusion processes

Jump–diffusion processes involving diffusion processes with discontinuous movements, called jumps, are widely used to model time-series data that commonly exhibit discontinuity in their sample paths. The existing jump–diffusion models have been recently extended to multivariate time-series data. The models are, however, still limited by a single parametric jump-size distribution that is common across different subjects. Such strong parametric assumptions for the shape and structure of a jump-size distribution may be too restrictive and unrealistic for multiple subjects with different characteristics. This paper thus proposes an efficient Bayesian nonparametric method to flexibly model a jump-size distribution while borrowing information across subjects in a clustering procedure using a nested Dirichlet process. For efficient posterior computation, a partially collapsed Gibbs sampler is devised to fit the proposed model. The proposed methodology is illustrated through a simulation study and an application to daily stock price data for companies in the S&P 100 index from June 2007 to June 2017.     

Investigating dependence between frequency and severity via simple generalized linear models

Recently, a body of literature proposed new models relaxing a widely-used but controversial assumption of independence between claim frequency and severity in non-life insurance rate making. This paper critically reviews a generalized linear model approach, where a dependence between claim frequency and severity is introduced by treating frequency as a covariate in a regression model for severity. As an extension of this approach, we propose a dispersion model for severity. For this model, the information loss caused by using average severity rather than individual severity is examined in detail and the parameter estimators suffering from low efficiency are identified. We also provide analytical solutions for the aggregate sum to help rate making. We show that the simple functional form used in current research may not properly reflect the real underlying dependence structure. A real data analysis is given to explain our analytical findings.     

Affect in Daily Career Decision Self‐Efficacy and Career Choice Anxiety

The authors investigated the impact of emotional intelligence on 2 career decision variables: daily career decision self‐efficacy and daily career choice anxiety. They also examined the mediating effects of daily positive affect on these variables. At baseline, 103 Korean undergraduate and graduate students completed questionnaires about emotional intelligence. Using a daily diary method, the authors also collected data on participants’ daily positive affect, daily career decision self‐efficacy, and daily career choice anxiety for 21 consecutive days. Hierarchical linear model analyses indicated that emotional intelligence predicted daily career decision self‐efficacy and daily career choice anxiety. These findings suggest that fostering emotional intelligence and daily positive affect could help students determine their future career. For individuals experiencing difficulty in the career decision process, counselors may foster career decision self‐efficacy and reduced career choice anxiety via enhancing clients’ emotional intelligence and daily positive affect.     

Self-efficacy and social support for psychological well-being of family caregivers of care recipients with dementia with Lewy bodies,Parkinson’s disease dementia,or Alzheimer’s disease

The study identified factors associated with depressive symptoms in family caregivers of persons with dementia with Lewy bodies, Alzheimer’s disease, or Parkinson’s disease dementia. An Internet-based survey generated 606 response sets. Regression analysis indicated that care recipients’ memory and behavioral problems, caregivers’ self-efficacy, grief, and tangible, emotional, and affectionate support were significantly associated with caregivers’ depressive symptoms. Self-efficacy mediated caregiver burden to improve psychological well-being. Social support mediated the association between caregiver grief and psychological well-being. Social workers can provide psychoeducational interventions to help caregivers to manage emotional distress and improve capacity to manage tasks related to the dementia.     

Productive Resistance within the Korean Public Sector: Exploring Organisational Culture

The article examines how South Korean civil servants responded to the introduction of pay for performance. Drawing upon 31 in-depth interviews with career civil servants, it identifies what became known as 1/n, a form of ‘discreet resistance’ that emerged and evolved. The analytical framework allows productive resistance to be seen as ebbing and flowing during organisational change that sees institutionalisation, deinstitutionalisation and re-institutionalisation. In understanding the cultural context of organisational resistance the contribution is three-fold. First, a nuanced definition and understanding of productive resistance. Second, it argues that productive resistance must be seen as part of a process that does not simply reflect ‘offer and counter-offer’ within the change management process. Thirdly, it identifies differences within groups and sub-cultures concerning commitment towards resistance and how these fissures contribute towards change as new interpretive schemes and justifications are presented in light of policy reformulations.     

SiZer analysis for the comparison of time series

SiZer (SIgnificant ZERo crossing of the derivatives) is a scale-space visualization tool for statistical inferences. In this paper we introduce a graphical device, which is based on SiZer, for the test of the equality of the mean of two time series. The estimation of the quantile in a confidence interval is theoretically justified by advanced distribution theory. The extension of the proposed method to the comparison of more than two time series is also done using residual analysis. A broad numerical study is conducted to demonstrate the sample performance of the proposed tool. In addition, asymptotic properties of SiZer for the comparison of two time series are investigated.     

Penalized likelihood approach for the four-parameter kappa distribution

The four-parameter kappa distribution (K4D) is a generalized form of some commonly used distributions such as generalized logistic, generalized Pareto, generalized Gumbel, and generalized extreme value (GEV) distributions. Owing to its flexibility, the K4D is widely applied in modeling in several fields such as hydrology and climatic change. For the estimation of the four parameters, the maximum likelihood approach and the method of L-moments are usually employed. The L-moment estimator (LME) method works well for some parameter spaces, with up to a moderate sample size, but it is sometimes not feasible in terms of computing the appropriate estimates. Meanwhile, using the maximum likelihood estimator (MLE) with small sample sizes shows substantially poor performance in terms of a large variance of the estimator. We therefore propose a maximum penalized likelihood estimation (MPLE) of K4D by adjusting the existing penalty functions that restrict the parameter space. Eighteen combinations of penalties for two shape parameters are considered and compared. The MPLE retains modeling flexibility and large sample optimality while also improving on small sample properties. The properties of the proposed estimator are verified through a Monte Carlo simulation, and an application case is demonstrated taking Thailand’s annual maximum temperature data.