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Objective: Recently, there has been a growing interest in operationalizing and measuring cognitive reserve (CR) for clinical and research purposes. This umbrella review aims to summarize the existing systematic and meta-analytic reviews about measures of CR. Method: A literature search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the guidelines of Aromataris et al. (2015) to identify the systematic reviews and meta-analysis involving the assessment of CR. The methodological quality of the papers included in this umbrella review was assessed with A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR-2) and Specialist Unit for Review Evidence (SURE). Results: Thirty-one reviews were identified, sixteen of which were systematic reviews, and fifteen were meta-analyses. Most of the reviews had a critically low quality, according to AMSTAR-2. The reviews included between two and 135 studies. Most of the papers focused on older adults, mainly those with dementia. CR was measured using one to six proxies, but most considered each proxy separately. The most assessed proxies of CR were education on its own, combined with occupation and/or engagement in activities or combined with parental education, bilingualism, and engagement in activities when four CR proxies were studied. Most of the studies included in higher quality reviews focused on three proxies, with education and engagement in activities being the most evaluated using CR questionnaires. Conclusion: Despite the growing interest in measuring CR, its operationalization did not improve since the last umbrella review in this field. [ABSTRACT FROM AUTHOR]

Risky and aggressive driving behaviors remain a serious safety concern across the globe. While engineering, education, and emergency medical services can affect the outcome of these behaviors, enforcement is a critical element of a well-rounded safety focus and has been proven to significantly reduce serious injury and fatal crashes. To appropriately and equitably allocate enforcement resources within a region, agencies must understand the relationship of crash locations (e.g., opportunities for improvement) and citation data (e.g., utilization of existing resources). To investigate this relationship, a case study approach was used to develop seemingly unrelated regression equation (SURE) models based on community attributes to predict crash and citation rates in the selected region. Through their application, regions that were overrepresented and underrepresented in their citation and crash rates were revealed. A geographic information system was used to illustrate these relationships using a combination of preliminary geospatial maps and maps based on the applied regression models. The resulting analysis revealed explanatory maps demonstrating potential enforcement gaps. The factors that most influence crash and citation rates were also revealed. This research provides a foundation for officials to improve the allocation of enforcement-related resources to equitably improve safety within a region. [ABSTRACT FROM AUTHOR]

Nearly all estimators in statistical prediction come with an associated tuning parameter, in one way or another. Common practice, given data, is to choose the tuning parameter value that minimizes a constructed estimate of the prediction error of the estimator; we focus on Stein's unbiased risk estimator, or SURE, which forms an unbiased estimate of the prediction error by augmenting the observed training error with an estimate of the degrees of freedom of the estimator. Parameter tuning via SURE minimization has been advocated by many authors, in a wide variety of problem settings, and in general, it is natural to ask: what is the prediction error of the SURE-tuned estimator? An obvious strategy would be simply use the apparent error estimate as reported by SURE, that is, the value of the SURE criterion at its minimum, to estimate the prediction error of the SURE-tuned estimator. But this is no longer unbiased; in fact, we would expect that the minimum of the SURE criterion is systematically biased downwards for the true prediction error. In this work, we define the excess optimism of the SURE-tuned estimator to be the amount of this downward bias in the SURE minimum. We argue that the following two properties motivate the study of excess optimism: (i) an unbiased estimate of excess optimism, added to the SURE criterion at its minimum, gives an unbiased estimate of the prediction error of the SURE-tuned estimator; (ii) excess optimism serves as an upper bound on the excess risk, that is, the difference between the risk of the SURE-tuned estimator and the oracle risk (where the oracle uses the best fixed tuning parameter choice). We study excess optimism in two common settings: shrinkage estimators and subset regression estimators. Our main results include a James–Stein-like property of the SURE-tuned shrinkage estimator, which is shown to dominate the MLE; and both upper and lower bounds on excess optimism for SURE-tuned subset regression. In the latter setting, when the collection of subsets is nested, our bounds are particularly tight, and reveal that in the case of no signal, the excess optimism is always in between 0 and 10 degrees of freedom, regardless of how many models are being selected from. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

For two groups of post-communist countries (CEE and CIS) we estimated the parameters of convergence equations on the basis of annual data. We depart from standard econometric theory, which involves panel regression techniques. We test cross-country heterogeneity of parameters within a system of Seemingly Unrelated Regression Equations (SURE). We show empirical evidence in favour of the variability of parameters describing the convergence effect and productivity growth rates across countries. Our approach seems a convincing alternative to the panel regression approach where random effects can be estimated, imposing an assumption about the constancy of structural parameters within the group of countries under analysis. We discuss the role of the global financial crisis in the heterogeneity of convergence processes and productivity at the country level. The aforementioned SURE model was estimated based on two datasets, one containing observations prior to the crisis and the second containing the whole sample. [ABSTRACT FROM AUTHOR]

We use Seemingly Unrelated Regressions Estimation methods to assess the link between prices, bond yields and the fiscal behavior. A first equation determines the country-specific cost of government financing via the long-term government bond yield, as a function of budget balance positions. A second equation links the price level to the cost of government financing. Our results for 15 EU countries in the period 1980Q1–2013Q4 show that improvements in the fiscal stance lead to persistent falls in sovereign yields; higher sovereign yields are reflected in upward price movements; improvements in the fiscal stance in recession times lead to short-term decreases in yields and better fiscal stance in expansions induce downward movement in bond yields only after 8 quarters. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between medical treatment costs and the length of hospital stays resulting from motorcycle crashes involving the elderly. The World Health Organization defines ‘elderly’ as people more than 65 years old. The sample for this study consisted of data for the year 2007 collected by the Bureau of National Health Insurance, Taiwan. We develop models for predicting medical costs and the length of hospital stays based on diagnosis, hospital and user types. The seemingly unrelated regression equation (SURE) model was applied first to investigate the relationship between medical costs and the length of hospital stays. The SURE model shows that the type of injury (e.g. head injury) is statistically significant and has positive effects on medical costs for motorcycle crashes involving the elderly in Taiwan. Due to the statistical insignificance of the dependency between medical costs and length of hospital stays, two separate simple linear regression models were subsequently estimated. For motorcycle crashes, patients over 80 years old had the highest medical costs. The findings reinforce the need for transportation authorities to focus on preventing certain types of injuries that are particularly serious and costly for the elderly in Taiwan. [ABSTRACT FROM AUTHOR]

This article uses a consumer theory-based systemic approach to model the demand for monetary liquid asset holdings in Chile. We implement the suggestions and caveats of aggregation theory for the estimation of a demand system for liquid assets (monies) in static, dynamic and time-varying parameters setups. Our results are robust and theoretically consistent with consumer theory restrictions, as a system derived from a utility maximizing framework and a quasi concave utility function. In our estimations, we find stability of interest rate elasticities, in contrast to previous related literature. We also document evidence that long (short) maturity rates are associated to less (more) liquid assets. [ABSTRACT FROM PUBLISHER]

Having constructed a data-based estimation rule, perhaps a logistic regression or a classification tree, the statistician would like to know its performance as a predictor of future cases. There are two main theories concerning prediction error: (1) penalty methods such as Cp, Akaike's information criterion, and Slein's unbiased risk estimate that depend on the covariance between data points and their corresponding predictions; and (2) cross-validation and related nonparametric bootstrap techniques. This article concerns the connection between the two theories. A Rao-Blackwell type of relation is derived in which nonparametric methods such as cross-validation are seen to be randomized versiotis of their covariance penalty counterparts. The model-based penalty methods other substantially better accuracy, assuming that the model is believable. [ABSTRACT FROM AUTHOR]