Admissible linear estimation in singular linear models with respect to a restricted parameter set

The admissibility results of Hoffmann (1977), proved in the context of a nonsingular covariance matrix are extended to the situation where the covariance matrix is singular. Admissible linear estimators in the Gauss-Markoff model are characterised and admissibility of the Best Linear Unbiased Estimator is investigated.     

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.     

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.     

Prominence and exclusivity: Identity and opposition to immigration in a multinational context

Using a framework derived from Identity Theory, this work focuses on the extent to which attitudes towards immigration are structured by the prominence and exclusivity of national identity. Prominence refers to the value of an identity relative to others. Exclusivity is when the prominence of a single identity negates all other comparable identities. Prominence and exclusivity are relevant for understanding attitudes towards immigration in that they imply degrees of flexibility on the very dimension of identity that most distinguishes immigrants—nationality. Catalonia, an autonomous region of Northwestern Spain that is home to multiple autochthonous identities and a recent large influx of immigrants, allows the role of identity exclusivity and prominence to be directly assessed. Using a representative survey of attitudes towards immigration collected in 2010 (n?=?1389), results show that, contrary to expectations, exclusive and prominent identifiers (i.e. only Spanish or only Catalan) are not significantly more opposed to immigration. Future work should consider national identity to be a relevant contextual dimension not for its independent predictive power, but because of its intersection with other salient predictors such as education, employment and class.     

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.     

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.     

Family and child characteristics linking neighborhood context and child externalizing behavior

Neighborhood conditions are related to children's externalizing behavior, although few processes that help explain this association have been identified. With data from 189 primarily low‐income Anglo and Mexican American families, we tested a stress process model that included 3 potential mediators of this relationship. The results showed that child stressful life events, association with deviant peers, and parent‐child conflict mediated the relationship between neighborhood context and child externalizing behavior when household income and maternal depression were controlled. The model explained more than 25% of the variance in externalizing behavior. Furthermore, differences in results for families with a U.S.‐born versus Mexico‐born mother showed that neighborhood influences on families and children may be quite complex.     

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.