Moment conditions for the quadratic regression model with measurement error

We consider a new estimator for the quadratic errors-in-variables model that exploits higher-order moment conditions under the assumption that the distribution of the measurement error is symmetric and free of excess kurtosis. Our approach contributes to the literature by not requiring any side information and by straightforwardly allowing for one or more error-free control variables. We propose a Wald-type statistical test, based on an auxiliary method-of-moments estimator, to verify a necessary condition for our estimator's consistency. We derive the asymptotic properties of the estimator and the statistical test and illustrate their finite-sample properties by means of a simulation study and an empirical application to existing data from the literature. Our simulations show that the method-of-moments estimator performs well in terms of bias and variance and even exhibits a certain degree of robustness to the distributional assumptions about the measurement error. In the simulation experiments where such robustness is not present, our statistical test already has high power for relatively small samples

Predictive validity of small-sided game performance and physical tests for overall performance ratings in soccer:Presentation

Introduction:Talent identification research in soccer involves the prediction of elite soccer performance. Although conventional research has mainly focused on isolated (e.g., physiological) components as predictors of performance level, representative samples of in-game soccer behavior may improve predictions [1]. As part of a longitudinal study, this pilot analysis compared the predictive validity of representative samples of soccer performance in small sided games (SSGs), against the validity of performance on an endurance, sprint and agility test. Method: Sixteen players of the U-19 team and 18 players of the U-23 team of a professional soccer academy participated in 11 (SD = 3) different 7-vs-7 games. Player’s performance was assessed based on the result of each SSG through two measures: the average number of points obtained in an SSG (3 points for a win system), and an Elo-rating. Standardized tests were used to assess speed, endurance, and agility. Whole-season performance ratings (0 – 10) given by coaches at the end of the season were used as a criterion measure. Results and discussion: For the U-19 players, average points per SSG (r = 0.44) and Elo-rating (r = 0.49) were substantially stronger predictors of the coaches’ rating than speed (r = -0.08), endurance (r = 0.34), and agility (r = -0.08). Contrastingly, for the U-23 players, all variables correlated negatively with coaches’ rating. Average points per SSG (r = -0.40), Elo-rating (r = -0.32), and speed (r = -0.42), had the strongest negative correlations. Endurance (r = -0.12) and agility (r = -0.08) had small correlations. Possibly, the contradictory findings may reflect differences in the construction of ratings in each group. Despite inconclusive evidence, part of the results supports similar hypotheses in other domains [2].Conclusion:Preliminary findings suggest that representative samples from SSGs are potentially stronger indicators of soccer performance ratings than widely-used isolated components. However, given small sample sizes and the inconsistent results for the U-23 players, general inferences regarding the predictive value of this measure cannot be made yet.References:1.Den Hartigh RJR, Niessen ASM, Frencken WGP, Meijer RR. Selection procedures in sports: improvingpredictions of athletes’ future performance. Eur J Sport Sci. 2018;31:1–8.2.Lyons BD, Hoffman BJ, Michel JW, Williams KJ. On the predictive efficiency of past performance andphysical ability: The case of the national football league. Hum Perform.2011;24:158–7

Improving Talent Identification Using Insights from Selection Psychology:5-minute pitch

Talent identification involves the prediction of elite sports performance. According to a recent review, insights from selection psychology can help to provide more accurate predictions of future performance (Den Hartigh, Niessen, Frencken, & Meijer, 2018). One approach to potentially improve such predictions is using ‘sample-based’, as opposed to ‘sign-based’ methods. Sign-based methods are defined as tests that measure distinct (e.g., psychological) constructs that are conceptually related to the criterion. Sample-based methods aim to sample criterion behavior. I will present a study in which we compared the predictive validity of samples of soccer performance in small sided games (SSGs), against the validity of endurance, sprint and agility tests. Thirty-four players of the U-19 and U-23 teams of a professional soccer academy participated in 11 (SD = 3) 7-vs-7 games. Players’ performance was assessed based on their contribution to the result of each SSG outcomes, whereas standardized tests were used to assess speed, endurance, and agility. Whole-season performance ratings (0 – 10) by coaches were used as a criterion measure. For the U-19 players, SSG performance (r = .44) was a stronger predictor of the coaches’ performance rating than speed (r = -.08), endurance (r = .34), and agility (r = -.08). Surprisingly enough, all variables correlated negatively with coaches’ rating for the U-23 players, which might be due to other factors confounding the judgment of the coach in this age category. To conclude, our results provide first evidence that sample-based methods (SSGs) may provide stronger indicators of soccer performance than sign-based tests

LTSmin: high-performance language-independent model checking

In recent years, the LTSmin model checker has been extended with support for several new modelling languages, including probabilistic (Mapa) and timed systems (Uppaal). Also, connecting additional language front-ends or ad-hoc state-space generators to LTSmin was simplified using custom C-code. From symbolic and distributed reachability analysis and minimisation, LTSmin’s functionality has developed into a model checker with multi-core algorithms for on-the-fly LTL checking with partial-order reduction, and multi-core symbolic checking for the modal μ calculus, based on the multi-core decision diagram package Sylvan.\ud In LTSmin, the modelling languages and the model checking algorithms are connected through a Partitioned Next-State Interface (Pins), that allows to abstract away from language details in the implementation of the analysis algorithms and on-the-fly optimisations. In the current paper, we present an overview of the toolset and its recent changes, and we demonstrate its performance and versatility in two case studies

Identification of a possible superconducting transition above room temperature in natural graphite crystals

Measuring with high precision the electrical resistance of highly ordered natural graphite samples from a Brazil mine, we have identified a transition at $\sim$350~K with $\sim$40~K transition width. The step-like change in temperature of the resistance, its magnetic irreversibility and time dependence after a field change, consistent with trapped flux and flux creep, and the partial magnetic flux expulsion obtained by magnetization measurements, suggest the existence of granular superconductivity below 350~K. The zero-field virgin state can only be reached again after zero field cooling the sample from above the transition. Paradoxically, the extraordinarily high transition temperature we found for this and several other graphite samples is the reason why this transition remained undetected so far. The existence of well ordered rhombohedral graphite phase in all measured samples has been proved by x-rays diffraction measurements, suggesting its interfaces with the Bernal phase as a possible origin for the high-temperature superconductivity, as theoretical studies predicted. The localization of granular superconductivity at these two dimensional interfaces prevents the observation of a zero resistance state or of a full Meissner state.Comment: 14 pages with 21 figure