Elite players’ perceptions of football playing surfaces: a mixed effects ordinal logistic regression model of players’ perceptions

The aim of this study was to determine potential explanatory factors that may be associated with different attitudes amongst the global population of elite footballers to the use of different surfaces for football. A questionnaire was used to capture elite football players’ perceptions of playing surfaces and a mixed effects ordinal logistic regression model was used to explore potential explanatory factors of players’ perceptions. In total, responses from 1129 players from 44 different countries were analysed. The majority of players expressed a strong preference for the use of Natural Turf pitches over alternatives such as Artificial Turf. The regression model, with a players’ country as a random effect, indicated that players were less favourable towards either Natural Turf or Artificial Turf where there was perceived to be greater variability in surface qualities or the surface was perceived to have less desirable properties. Player’s surface experience was also linked to their overall attitudes, with a suggestion that the quality of the Natural Turf surface players experienced dictated players’ support for Artificial Turf.     

Good item or bad—can latent class analysis tell?: the utility of latent class analysis for the evaluation of survey questions

Summary.  Latent class analysis has been used to model measurement error, to identify flawed survey questions and to estimate mode effects. Using data from a survey of University of Maryland alumni together with alumni records, we evaluate this technique to determine its usefulness for detecting bad questions in the survey context. Two sets of latent class analysis models are applied in this evaluation: latent class models with three indicators and latent class models with two indicators under different assumptions about prevalence and error rates. Our results indicated that the latent class analysis approach produced good qualitative results for the latent class models—the item that the model deemed the worst was the worst according to the true scores. However, the approach yielded weaker quantitative estimates of the error rates for a given item.     

In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?

Abstract

It is widely known that significant in-sample evidence of predictability does not guarantee significant out-of-sample predictability. This is often interpreted as an indication that in-sample evidence is likely to be spurious and should be discounted. In this paper, we question this interpretation. Our analysis shows that neither data mining nor dynamic misspecification of the model under the null nor unmodelled structural change under the null are plausible explanations of the observed tendency of in-sample tests to reject the no-predictability null more often than out-of-sample tests. We provide an alternative explanation based on the higher power of in-sample tests of predictability in many situations. We conclude that results of in-sample tests of predictability will typically be more credible than results of out-of-sample tests.     

The exponentiated Fréchet regression: an alternative model for actuarial modelling purposes

ABSTRACT

In this paper we introduce the exponentiated Fréchet regression for modelling positive responses having a long-tailed distribution in a regression model, which are common in actuarial statistics. We propose two parameterizations each of which links the regression parameters with the explanatory variables. We then discuss the maximum likelihood estimation of the parameters both theoretically and empirically. In order to meet the needs of an actuary, closed-form expressions for certain risk measures for the exponentiated Fréchet distribution are also derived. We employ the proposed model to a motorcycle claim size data set.     

ML estimation for factor analysis: EM or non-EM?

To obtain maximum likelihood (ML) estimation in factor analysis (FA), we propose in this paper a novel and fast conditional maximization (CM) algorithm, which has quadratic and monotone convergence, consisting of a sequence of CM log-likelihood (CML) steps. The main contribution of this algorithm is that the closed form expression for the parameter to be updated in each step can be obtained explicitly, without resorting to any numerical optimization methods. In addition, a new ECME algorithm similar to Liu’s (Biometrika 81, 633–648, 1994) one is obtained as a by-product, which turns out to be very close to the simple iteration algorithm proposed by Lawley (Proc. R. Soc. Edinb. 60, 64–82, 1940) but our algorithm is guaranteed to increase log-likelihood at every iteration and hence to converge. Both algorithms inherit the simplicity and stability of EM but their convergence behaviors are much different as revealed in our extensive simulations: (1) In most situations, ECME and EM perform similarly; (2) CM outperforms EM and ECME substantially in all situations, no matter assessed by the CPU time or the number of iterations. Especially for the case close to the well known Heywood case, it accelerates EM by factors of around 100 or more. Also, CM is much more insensitive to the choice of starting values than EM and ECME.     

Revisiting the Berger location model: Fallacious confidence interval or a rigged example?

Since the 1960s the Bayesian case against frequentist inference has been partly built on several “classic” examples which are devised to show how frequentist inference procedures can give rise to fallacious results; see Berger and Wolpert (1988) [2]. The primary aim of this note is to revisit one of these examples, the Berger location model, that is supposed to demonstrate the fallaciousness of frequentist Confidence Interval (CI) estimation. A closer look at the example, however, reveals that the fallacious results stem primarily from the problematic nature of the example itself, since it is based on a non-regular probability model that enables one to (indirectly) assign probabilities to the unknown parameter. Moreover, the proposed confidence set is not a proper frequentist CI in the sense that it is not defined in terms of legitimate error probabilities.     

Sodium cromoglicate: an ineffective drug or meta‐analysis misused?

Sodium cromoglicate (SCG) has been available since around 1970 for the treatment of asthma and other allergic disorders in both adults and children. It has been approved for use around the world. Over the period of its development, a number of different formulations were introduced. In 1999, a systematic review of SCG use in childhood asthma was carried out and reported initially as a poster. Further systematic reviews and papers followed from the same authors and finally a Cochrane Collaboration review was published in 2003. All concluded that SCG was ineffective in paediatric asthma. Both the British Thoracic Society Guidelines for the treatment of paediatric asthma and the Model List of Essential Drugs of the WHO now reflect these conclusions. This paper looks carefully at the conclusions of these systematic reviews and raises concerns about the interpretation of the results. These failed to take adequate account of the changes with time in both the formulations used and the age groups examined, and also failed to take adequate note of the totality of information available over all end-points. One primary end-point was based on only four out of the 24 studies included in the review. Rather than having no effect, it is demonstrated that a considerable body of evidence favours SCG compared to placebo and, far from being ineffective, the drug appears to be effective particularly in older children. This article replaces a previously published version. DOI: 10.1002/pst.258.     

Which Measure Should be Used for Testing in a Paired Design: Simple Difference,Percent Change,or Symmetrized Percent Change?

We aimed to determine the most proper change measure among simple difference, percent, or symmetrized percent changes in simple paired designs. For this purpose, we devised a computer simulation program. Since distributions of percent and symmetrized percent change values are skewed and bimodal, paired t-test did not give good results according to Type I error and the test power. To be to able use percent change or symmetrized percent change as change measure, either the distribution of test statistics should be transformed to a known theoretical distribution by transformation methods or a new test statistic for these values should be developed.     

A fast Monte Carlo expectation–maximization algorithm for estimation in latent class model analysis with an application to assess diagnostic accuracy for cervical neoplasia in women with atypical glandular cells

In this article, we use a latent class model (LCM) with prevalence modeled as a function of covariates to assess diagnostic test accuracy in situations where the true disease status is not observed, but observations on three or more conditionally independent diagnostic tests are available. A fast Monte Carlo expectation–maximization (MCEM) algorithm with binary (disease) diagnostic data is implemented to estimate parameters of interest; namely, sensitivity, specificity, and prevalence of the disease as a function of covariates. To obtain standard errors for confidence interval construction of estimated parameters, the missing information principle is applied to adjust information matrix estimates. We compare the adjusted information matrix-based standard error estimates with the bootstrap standard error estimates both obtained using the fast MCEM algorithm through an extensive Monte Carlo study. Simulation demonstrates that the adjusted information matrix approach estimates the standard error similarly with the bootstrap methods under certain scenarios. The bootstrap percentile intervals have satisfactory coverage probabilities. We then apply the LCM analysis to a real data set of 122 subjects from a Gynecologic Oncology Group study of significant cervical lesion diagnosis in women with atypical glandular cells of undetermined significance to compare the diagnostic accuracy of a histology-based evaluation, a carbonic anhydrase-IX biomarker-based test and a human papillomavirus DNA test.     

Sampling Returns for Realized Variance Calculations: Tick Time or Transaction Time?

This article introduces a new model for transaction prices in the presence of market microstructure noise in order to study the properties of the price process on two different time scales, namely, transaction time where prices are sampled with every transaction and tick time where prices are sampled with every price change. Both sampling schemes have been used in the literature on realized variance, but a formal investigation into their properties has been lacking. Our empirical and theoretical results indicate that the return dynamics in transaction time are very different from those in tick time and the choice of sampling scheme can therefore have an important impact on the properties of realized variance. For RV we find that tick time sampling is superior to transaction time sampling in terms of mean-squared-error, especially when the level of noise, number of ticks, or the arrival frequency of efficient price moves is low. Importantly, we show that while the microstructure noise may appear close to IID in transaction time, in tick time it is highly dependent. As a result, bias correction procedures that rely on the noise being independent, can fail in tick time and are better implemented in transaction time.     

Sampling Returns for Realized Variance Calculations: Tick Time or Transaction Time?

This article introduces a new model for transaction prices in the presence of market microstructure noise in order to study the properties of the price process on two different time scales, namely, transaction time where prices are sampled with every transaction and tick time where prices are sampled with every price change. Both sampling schemes have been used in the literature on realized variance, but a formal investigation into their properties has been lacking. Our empirical and theoretical results indicate that the return dynamics in transaction time are very different from those in tick time and the choice of sampling scheme can therefore have an important impact on the properties of realized variance. For RV we find that tick time sampling is superior to transaction time sampling in terms of mean-squared-error, especially when the level of noise, number of ticks, or the arrival frequency of efficient price moves is low. Importantly, we show that while the microstructure noise may appear close to IID in transaction time, in tick time it is highly dependent. As a result, bias correction procedures that rely on the noise being independent, can fail in tick time and are better implemented in transaction time.     

Control charts for high-quality processes: MAX or CUMAX?

For attribute data with (very) small failure rates control charts were introduced which are based on subsequent groups of r   failure times, for some r≥1$r\ge 1$. Within this family, it was shown to be attractive to stop once the maximum of such a group is sufficiently small, because this choice allows a very satisfactory nonparametric adaptation. The question we address here is whether a cumulative approach offers even further improvement. Thus instead of fixed groups, we shall use the first sequence of r consecutive sufficiently small failure times to produce a signal. A further reason for considering this type of chart is the fact that it forms the nonparametric counterpart of the well-known sets method.     

Evaluation strategies for case series: is Cox regression an alternative to the self controlled case series method for terminal events?

In this paper, we deal with the analysis of case series. The self-controlled case series method (SCCS) was developed to analyse the temporal association between time-varying exposure and an outcome event. We apply the SCCS method to the vaccination data of the German Examination Survey for Children and Adolescents (KiGGS). We illustrate that the standard SCCS method cannot be applied to terminal events such as death. In this situation, an extension of SCCS adjusted for terminal events gives unbiased point estimators. The key question of this paper is whether the general Cox regression model for time-dependent covariates may be an alternative to the adjusted SCCS method for terminal events. In contrast to the SCCS method, Cox regression is included in most software packages (SPSS, SAS, STATA, R, …) and it is easy to use. We can show that Cox regression is applicable to test the null hypothesis. In our KiGGS example without censored data, the Cox regression and the adjusted SCCS method yield point estimates almost identical to the standard SCCS method. We have conducted several simulation studies to complete the comparison of the two methods. The Cox regression shows a tendency to underestimate the true effect with prolonged risk periods and strong effects (Relative Incidence >2). If risk of the event is strongly affected by the age, the adjusted SCCS method slightly overestimates the predefined exposure effect. Cox regression has the same efficiency as the adjusted SCCS method in the simulation.