Combining multivariate density forecasts using predictive criteria

This paper combines multivariate density forecasts of output growth, inflationand interest rates from a suite of models. An out-of-sample weighting scheme based onthe predictive likelihood as proposed by Eklund and Karlsson (2005) and Andersson andKarlsson (2007) is used to combine the models. Three classes of models are considered: aBayesian vector autoregression (BVAR), a factor-augmented vector autoregression (FAVAR)and a medium-scale dynamic stochastic general equilibrium (DSGE) model. Using Australiandata, we find that, at short forecast horizons, the Bayesian VAR model is assignedthe most weight, while at intermediate and longer horizons the factor model is preferred.The DSGE model is assigned little weight at all horizons, a result that can be attributedto the DSGE model producing density forecasts that are very wide when compared withthe actual distribution of observations. While a density forecast evaluation exercise revealslittle formal evidence that the optimally combined densities are superior to those from thebest-performing individual model, or a simple equal-weighting scheme, this may be a resultof the short sample available.

Worst-case bounds for the logarithmic loss of predictors

We investigate on-line prediction of individual sequences. Given a class of predictors, the goal is to predict as well as the best predictor in the class, where the loss is measured by the self information (logarithmic) loss function. The excess loss (regret) is closely related to the redundancy of the associated lossless universal code. Using Shtarkov's theorem and tools from empirical process theory, we prove a general upper bound on the best possible (minimax) regret. The bound depends on certain metric properties of the class of predictors. We apply the bound to both parametric and nonparametric classes ofpredictors. Finally, we point out a suboptimal behavior of the popular Bayesian weighted average algorithm.

Improving small area estimation by combining surveys: new perspectives in regional statistics

A national survey designed for estimating a specific population quantity is sometimes used for estimation of this quantity also for a small area, such as a province. Budget constraints do not allow a greater sample size for the small area, and so other means of improving estimation have to be devised. We investigate such methods and assess them by a Monte Carlo study. We explore how a complementary survey can be exploited in small area estimation. We use the context of the Spanish Labour Force Survey (EPA) and the Barometer in Spain for our study.

Work values as predictors of entrepreneurial career intentions: A longitudinal analysis of gender effects

Purpose - Work values are an important characteristic to understand gender differences in career intentions, but how gender affects the relationship between values and career intentions is not well established. The purpose of this paper is to investigate whether gender moderates the effects of work values on level and change of entrepreneurial intentions (EI). Design/methodology/approach - In total, 218 German university students were sampled regarding work values and with EI assessed three times over the course of 12 months. Data were analysed with latent growth modelling. Findings - Self-enhancement and openness to change values predicted higher levels and conservation values lower levels of EI. Gender moderated the effects of enhancement and conservation values on change in EI. Research limitations/implications - The authors relied on self-reported measures and the sample was restricted to university students. Future research needs to verify to what extent these results generalize to other samples and different career fields, such as science or nursing. Practical implications - The results imply that men and women are interested in an entrepreneurial career based on the same work values but that values have different effects for men and women regarding individual changes in EI. The results suggest that the prototypical work values of a career domain seem important regarding increasing the career intent for the gender that is underrepresented in that domain. Originality/value - The results enhance understanding of how gender affects the relation of work values and a specific career intention, such as entrepreneurship.

Predictors and Moderators of Clinical Outcomes in Adolescents with Severe Mental Disorders After an Assertive Community Treatment.

Previous studies have shown that stressful life events (SLEs), gender, social functioning and pretreatment severity are some of the predictors and/or moderators of treatment outcome in psychiatric care. The current study explored the effect of these predictors and moderators on the treatment outcome related to assertive community treatment (ACT) proposed to young people with severe mental disorders. 98 patients were assessed for externalizing and emotional difficulties, at admission and then at discharge of an ACT. Analyses revealed significant improvements in terms of symptomatology. In particular, regression analyses showed that pretreatment severity is a significant predictor of the outcome on emotional symptoms and is moderated by SLE on the outcome on externalizing symptoms. Furthermore, higher social functioning proved to predict better outcome on externalizing symptoms. Our results further evidence that these factors can explain inter-individual differences in outcome related to ACT. The theoretical and clinical implications of these results are discussed.

Combining heat stress and moderate hypoxia reduces cycling time to exhaustion without modifying neuromuscular fatigue characteristics.

PURPOSE: This study investigated the isolated and combined effects of heat [temperate (22 °C/30 % rH) vs. hot (35 °C/40 % rH)] and hypoxia [sea level (FiO2 0.21) vs. moderate altitude (FiO2 0.15)] on exercise capacity and neuromuscular fatigue characteristics. METHODS: Eleven physically active subjects cycled to exhaustion at constant workload (66 % of the power output associated with their maximal oxygen uptake in temperate conditions) in four different environmental conditions [temperate/sea level (control), hot/sea level (hot), temperate/moderate altitude (hypoxia) and hot/moderate altitude (hot + hypoxia)]. Torque and electromyography (EMG) responses following electrical stimulation of the tibial nerve (plantar-flexion; soleus) were recorded before and 5 min after exercise. RESULTS: Time to exhaustion was reduced (P < 0.05) in hot (-35 ± 15 %) or hypoxia (-36 ± 14 %) compared to control (61 ± 28 min), while hot + hypoxia (-51 ± 20 %) further compromised exercise capacity (P < 0.05). However, the effect of temperature or altitude on end-exercise core temperature (P = 0.089 and P = 0.070, respectively) and rating of perceived exertion (P > 0.05) did not reach significance. Maximal voluntary contraction torque, voluntary activation (twitch interpolation) and peak twitch torque decreased from pre- to post-exercise (-9 ± 1, -4 ± 1 and -6 ± 1 % all trials compounded, respectively; P < 0.05), with no effect of the temperature or altitude. M-wave amplitude and root mean square activity were reduced (P < 0.05) in hot compared to temperate conditions, while normalized maximal EMG activity did not change. Altitude had no effect on any measured parameters. CONCLUSION: Moderate hypoxia in combination with heat stress reduces cycling time to exhaustion without modifying neuromuscular fatigue characteristics. Impaired oxygen delivery or increased cardiovascular strain, increasing relative exercise intensity, may have also contributed to earlier exercise cessation.

Predictors of leadership: The usual suspects and the suspect traits

In this chapter, I review literature on traits (i.e., individual differences) and their links to leader outcomes. I present an integrated model, the ascription-actuality trait theory, to explain two routes to leader outcomes that stem from traits: the route that objectively matters and the route that appears to matter but objectively may not. I discuss the history of trait research and provide criteria by which we should judge the validity of trait models. Finally, I review trait models that are the most predictive of leadership outcomes and identify those that are non-starters.

AWSum-Combining Classification with Knowledge Aquisition

Many classifiers achieve high levels of accuracy but have limited applicability in real world situations because they do not lead to a greater understanding or insight into the^way features influence the classification. In areas such as health informatics a classifier that clearly identifies the influences on classification can be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifier that provides accuracy comparable to other techniques whilst providing insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. The merits of this approach in classification and insight are evaluated on a Cystic Fibrosis and Diabetes datasets with positive results.

Inferring transport characteristics in a fractured rock aquifer by combining single-hole ground-penetrating radar reflection monitoring and tracer test data

Investigations of solute transport in fractured rock aquifers often rely on tracer test data acquired at a limited number of observation points. Such data do not, by themselves, allow detailed assessments of the spreading of the injected tracer plume. To better understand the transport behavior in a granitic aquifer, we combine tracer test data with single-hole ground-penetrating radar (GPR) reflection monitoring data. Five successful tracer tests were performed under various experimental conditions between two boreholes 6 m apart. For each experiment, saline tracer was injected into a previously identified packed-off transmissive fracture while repeatedly acquiring single-hole GPR reflection profiles together with electrical conductivity logs in the pumping borehole. By analyzing depth-migrated GPR difference images together with tracer breakthrough curves and associated simplified flow and transport modeling, we estimate (1) the number, the connectivity, and the geometry of fractures that contribute to tracer transport, (2) the velocity and the mass of tracer that was carried along each flow path, and (3) the effective transport parameters of the identified flow paths. We find a qualitative agreement when comparing the time evolution of GPR reflectivity strengths at strategic locations in the formation with those arising from simulated transport. The discrepancies are on the same order as those between observed and simulated breakthrough curves at the outflow locations. The rather subtle and repeatable GPR signals provide useful and complementary information to tracer test data acquired at the outflow locations and may help us to characterize transport phenomena in fractured rock aquifers.

Predictors of developmental dyslexia in European orthographies with varying complexity.

Background:  The relationship between phoneme awareness, rapid automatized naming (RAN), verbal short-term/working memory (ST/WM) and diagnostic category is investigated in control and dyslexic children, and the extent to which this depends on orthographic complexity. Methods:  General cognitive, phonological and literacy skills were tested in 1,138 control and 1,114 dyslexic children speaking six different languages spanning a large range of orthographic complexity (Finnish, Hungarian, German, Dutch, French, English). Results:  Phoneme deletion and RAN were strong concurrent predictors of developmental dyslexia, while verbal ST/WM and general verbal abilities played a comparatively minor role. In logistic regression models, more participants were classified correctly when orthography was more complex. The impact of phoneme deletion and RAN-digits was stronger in complex than in less complex orthographies. Conclusions:  Findings are largely consistent with the literature on predictors of dyslexia and literacy skills, while uniquely demonstrating how orthographic complexity exacerbates some symptoms of dyslexia.

A prospective study of predictors of poststroke depression.

OBJECTIVE: To investigate the association between early depressive behavior after stroke onset and occurrence of poststroke depression (PSD) at 3- and 12-month follow-up evaluations. METHODS: The study prospectively included 273 patients with first-ever single uncomplicated ischemic stroke. In the stroke unit, nurses scored crying, overt sadness, and apathy daily using an observational method to include patients with comprehension deficits. The Barthel Index was used to assess disability. Follow-up evaluation at months 3 and 12 included psychiatric assessment based on the Diagnostic and Statistical Manual of Mental Disorders, 4th edition. RESULTS: Crying (19.8%), overt sadness (50.5%), and apathy (47.6%) were observed. Of the patients observed crying, 4 showed pathologic crying, 19 emotionalism, and 12 catastrophic reactions. Crying and overt sadness, but not apathy, were associated with a subjective experience of depression (p < 0.05). Thirty of 52 (58%) patients observed crying, 12 of 19 (63%) patients with emotionalism, and 5 of 12 (41%) patients with catastrophic reactions developed PSD within the first year. Multiple logistic regression analysis showed that only severe functional disability (odds ratio [OR], 4.31; 95% CI, 2.41 to 7.69), crying behaviors (OR, 2.66; 95% CI, 1.35 to 5.27), and an age <68 years (OR, 2.32; 95% CI, 1.30 to 4.13) were (p < 0.05) predictors of late PSD development (13% of the variance). CONCLUSIONS: In the stroke unit, crying and overt sadness are more reliable indicators of depressed mood than apathy. In patients with first-ever stroke, crying behaviors soon after stroke, a younger age, and severe disability are predictors of poststroke depression occurrence within the first year after stroke onset.

The impact of cyclin D1 mRNA isoforms, morphology and p53 in mantle cell lymphoma: p53 alterations and blastoid morphology are strong predictors of a high proliferation index.

BACKGROUND: Mantle cell lymphoma is a clinically heterogeneous disease characterized by overexpression of cyclin D1 protein. Blastoid morphology, high proliferation, and secondary genetic aberrations are markers of aggressive behavior. Expression profiling of mantle cell lymphoma revealed that predominance of the 3'UTR-deficient, short cyclin D1 mRNA isoform was associated with high cyclin D1 levels, a high "proliferation signature" and poor prognosis. DESIGN AND METHODS: Sixty-two cases of mantle cell lymphoma were analyzed for cyclin D1 mRNA isoforms and total cyclin D1 levels by real-time reverse transcriptase polymerase chain reaction, and TP53 alterations were assessed by immunohistochemistry and molecular analysis. Results were correlated with proliferation index and clinical outcome. RESULTS: Predominance of the short cyclin D1 mRNA was found in 14 (23%) samples, including four with complete loss of the standard transcript. TP53 alterations were found in 15 (24%) cases. Predominance of 3'UTR-deficient mRNA was significantly associated with high cyclin D1 mRNA levels (P=0.009) and more commonly found in blastoid mantle cell lymphoma (5/11, P=0.060) and cases with a proliferation index of >20% (P=0.026). Both blastoid morphology (11/11, P<0.001) and TP53 alterations (15/15, P<0.001) were significantly correlated with a high proliferation index. A proliferation index of 10% was determined to be a significant threshold for survival in multivariate analysis (P=0.01). CONCLUSIONS: TP53 alterations are strongly associated with a high proliferation index and aggressive behavior in mantle cell lymphoma. Predominance of the 3'UTR-deficient transcript correlates with higher cyclin D1 levels and may be a secondary contributing factor to high proliferation, but failed to reach prognostic significance in this study.

Predictors of poor response to methotrexate in polyarticular-course juvenile idiopathic arthritis: analysis of the PRINTO methotrexate trial.

OBJECTIVES: To determine whether baseline demographic, clinical, articular and laboratory variables predict methotrexate (MTX) poor response in polyarticular-course juvenile idiopathic arthritis. METHODS: Patients newly treated for 6 months with MTX enrolled in the Paediatric Rheumatology International Trials Organization (PRINTO) MTX trial. Bivariate and logistic regression analyses were used to identify baseline predictors of poor response according to the American College of Rheumatology pediatric (ACR-ped) 30 and 70 criteria. RESULTS: In all, 405/563 (71.9%) of patients were women; median age at onset and disease duration were 4.3 and 1.4 years, respectively, with anti-nuclear antibody (ANA) detected in 259/537 (48.2%) patients. With multivariate logistic regression analysis, the most important determinants of ACR-ped 70 non-responders were: disease duration > 1.3 years (OR 1.93), ANA negativity (OR 1.77), Childhood Health Assessment Questionnaire (CHAQ) disability index > 1.125 (OR 1.65) and the presence of right and left wrist activity (OR 1.55). Predictors of ACR-ped 30 non-responders were: ANA negativity (OR 1.92), CHAQ disability index > 1.14 (OR 2.18) and a parent's evaluation of child's overall well-being < or = 4.69 (OR 2.2). CONCLUSION: The subgroup of patients with longer disease duration, ANA negativity, higher disability and presence of wrist activity were significantly associated with a poorer response to a 6-month MTX course.