Nonparametric Estimation of the Average Availability

The average availability of a repairable system is the expected proportion of time that the system is operating in the interval [0, t]. The present article discusses the nonparametric estimation of the average availability when (i) the data on ‘n’ complete cycles of system operation are available, (ii) the data are subject to right censorship, and (iii) the process is observed upto a specified time ‘T’. In each case, a nonparametric confidence interval for the average availability is also constructed. Simulations are conducted to assess the performance of the estimators.     

A Bayesian hierarchical mixture model for platelet-derived growth factor receptor phosphorylation to improve estimation of progression-free survival in prostate cancer

Summary.  Advances in understanding the biological underpinnings of many cancers have led increasingly to the use of molecularly targeted anticancer therapies. Because the platelet-derived growth factor receptor (PDGFR) has been implicated in the progression of prostate cancer bone metastases, it is of great interest to examine possible relationships between PDGFR inhibition and therapeutic outcomes. We analyse the association between change in activated PDGFR (phosphorylated PDGFR) and progression-free survival time based on large within-patient samples of cell-specific phosphorylated PDGFR values taken before and after treatment from each of 88 prostate cancer patients. To utilize these paired samples as covariate data in a regression model for progression-free survival time, and be cause the phosphorylated PDGFR distributions are bimodal, we first employ a Bayesian hierarchical mixture model to obtain a deconvolution of the pretreatment and post-treatment within-patient phosphorylated PDGFR distributions. We evaluate fits of the mixture model and a non-mixture model that ignores the bimodality by using a supnorm metric to compare the empirical distribution of each phosphorylated PDGFR data set with the corresponding fitted distribution under each model. Our results show that first using the mixture model to account for the bimodality of the within-patient phosphorylated PDGFR distributions, and then using the posterior within-patient component mean changes in phosphorylated PDGFR so obtained as covariates in the regression model for progression-free survival time, provides an improved estimation.     

Nonlinear Autoregression with Positive Innovations

Use of nonlinear models in analyzing time series data is becoming increasingly popular. This paper considers a broad class of nonlinear autoregressive models where the autoregressive part is additive and the terms are nonlinear functions of the past data. Also, the innovation distribution is supported on the non-negative reals and satisfies a tail regularity condition. The linear parameters of the autoregression are estimated using a linear programming recipe which yields much more accurate estimates than traditional methods such as conditional least squares. Limiting distribution of the linear programming estimators is obtained. Simulation studies validate the asymptotic results and reveal excellent small sample properties of the LPE estimator.     

A generalized pivotal quantity approach to portfolio selection

The major problem of mean–variance portfolio optimization is parameter uncertainty. Many methods have been proposed to tackle this problem, including shrinkage methods, resampling techniques, and imposing constraints on the portfolio weights, etc. This paper suggests a new estimation method for mean–variance portfolio weights based on the concept of generalized pivotal quantity (GPQ) in the case when asset returns are multivariate normally distributed and serially independent. Both point and interval estimations of the portfolio weights are considered. Comparing with Markowitz's mean–variance model, resampling and shrinkage methods, we find that the proposed GPQ method typically yields the smallest mean-squared error for the point estimate of the portfolio weights and obtains a satisfactory coverage rate for their simultaneous confidence intervals. Finally, we apply the proposed methodology to address a portfolio rebalancing problem.     

Income Inequality and Political Participation: A District-Level Analysis of Hong Kong Elections

Social Indicators Research - Income inequality has long been suggested as a crucial factor in determining political participation. This study focuses on the case of Hong Kong, a city with...     

Marginal Trust in Risk Managers: Building and Losing Trust Following Decisions Under Uncertainty

Although a considerable amount of research has examined correlates of baseline public trust in risk managers, much less research has looked at marginal changes in public trust following specific events. Such research is important for identifying what kinds of events will lead to increases and decreases in public trust and thus for understanding how trust is built and lost. Using a taxonomy based upon signal detection theory (SDT), the current article presents two experimental studies examining marginal trust change following eight different types of events. Supporting predictions, cautious decisionmakers who accepted signs of danger (Hits and False Alarms) were more likely to be trusted than those who rejected them (All Clears and Misses). Moreover, transparency about an event was associated with higher levels of marginal trust than a lack of transparency in line with earlier findings. Contrary to predictions, however, trust was less affected by whether the decisions were correct (i.e., Hits and All Clears) or incorrect (i.e., False Alarms and Misses). This finding was primarily due to a "False Alarm Effect" whereby Open False Alarms led to positive increases in trust despite being incorrect assessments of risk. Results are explained in terms of a cue diagnosticity account of impression formation and suggest that a taxonomy of event types based on SDT may be useful in furthering our understanding of how public trust in risk managers is gained and lost.     

Comparative Cases of Conscience: Teaching Josiah Royce’s Philosophy of Loyalty in China

This essay first describes the distinctively American character of the life and thought of Josiah Royce, conceiving of Royce as equal parts Puritan and pioneer. Next, the author’s recent experience teaching Royce’s philosophy of loyalty in China is discussed, highlighting pedagogical challenges encountered and techniques employed to navigate these hurdles. A summary of Royce’s philosophy of loyalty is given, incorporating examples used in the classroom. It is shown that upon applying Royce’s philosophy of loyalty to real life situations, resonances between classical Chinese (Confucian) and classical American moral philosophies emerge. It is argued that Royce’s philosophy of loyalty cuts across cultural lines, serving as a viable and valuable theoretical resource for negotiating contemporary moral dilemmas, irrespective of geographical location.     

Examination of the neural substrates activated in memories of experiences with resonant and dissonant leaders

Given the relevance of leadership in organizational life, we designed an exploratory study to assess the neural mechanisms involved in memories of interactions with resonant and dissonant leaders (a follower-centric study). Subjects in advanced professional roles were asked about previous incidents with both types of leaders, and functional magnetic resonance imaging (fMRI) scans were then conducted with cues developed from these recollections. Recalling experiences with resonant leaders activated neural areas such as the bilateral insula, right inferior parietal lobe, and left superior temporal gyrus; regions associated with the mirror neuron system, default mode or social network, and positive affect. Recalling experiences with dissonant leaders negatively activated the right anterior cingulate cortex and activated the right inferior frontal gyrus, bilateral posterior region of the inferior frontal gyrus, and bilateral inferior frontal gyrus/insula; regions associated with the mirror neuron system and related to avoidance, narrowed attention, decreased compassion, and negative emotions.     

Characterizing Discrete Life Distributions by Properties of Reversed Variance Residual life

The present article discusses the characterization of non negative integer-valued random variable using reversed variance residual life. A special attention is given to the characterizations by relationship between conditional variance and the reversed failure rate. A lower bound to the conditional variance is also established. Our bound is compared to the Cramer-Rao and Chapman-Robbins lower bounds so that construction of minimum variance unbiased estimators of relevant parametric functions in truncated distributions can be possible.