Hierarchical Bayesian Modeling of Hitting Performance in Baseball

We have developed a sophisticated statistical model for predicting the hitting performance of Major League baseball players. The Bayesian paradigm provides a principled method for balancing past performance with crucial covariates, such as player age and position. We share information across time and across players by using mixture distributions to control shrinkage for improved accuracy. We compare the performance of our model to current sabermetric methods on a held-out season (2006), and discuss both successes and limitations

Count Models Based on Weibull Interarrival Times

The widespread popularity and use of both the Poisson and the negative binomial models for count data arise, in part, from their derivation as the number of arrivals in a given time period assuming exponentially distributed interarrival times (without and with heterogeneity in the underlying base rates, respectively). However, with that clean theory come some limitations including limited flexibility in the assumed underlying arrival rate distribution and the inability to model underdispersed counts (variance less than the mean). Although extant research has addressed some of these issues, there still remain numerous valuable extensions. In this research, we present a model that, due to computational tractability, was previously thought to be infeasible. In particular, we introduce here a generalized model for count data based upon an assumed Weibull interarrival process that nests the Poisson and negative binomial models as special cases. The computational intractability is overcome by deriving the Weibull count model using a polynomial expansion which then allows for closed-form inference (integration term-by-term) when incorporating heterogeneity due to the conjugacy of the expansion and a commonly employed gamma distribution. In addition, we demonstrate that this new Weibull count model can (1) model both over- and underdispersed count data, (2) allow covariates to be introduced in a straightforward manner through the hazard function, and (3) be computed in standard software

Modelling mucociliary clearance

Mathematical modelling of the fluid mechanics of mucociliary clearance (MCC) is reviewed and future challenges for researchers are discussed. The morphology of the bronchial and tracheal airway surface liquid (ASL) and ciliated epithelium are briefly introduced. The cilia beat cycle, beat frequency and metachronal coordination are described, along with the rheology of the mucous layer. Theoretical modelling of MCC from the late 1960s onwards is reviewed, and distinctions between ‘phenomenological’, ‘slender body theory’ and recent ‘fluid–structure interaction’ models are explained.\ud \ud The ASL consists of two layers, an overlying mucous layer and underlying watery periciliary layer (PCL) which bathes the cilia. Previous models have predicted very little transport of fluid in the PCL compared with the mucous layer. Fluorescent tracer transport experiments on human airway cultures conducted by Matsui et al. [Matsui, H., Randell, S.H., Peretti, S.W., Davis, C.W., Boucher, R.C., 1998. Coordinated clearance of periciliary liquid and mucus from airway surfaces. J. Clin. Invest. 102 (6), 1125–1131] apparently showed equal transport in both the PCL and mucous layer. Recent attempts to resolve this discrepancy by the present authors are reviewed, along with associated modelling findings. These findings have suggested new insights into the interaction of cilia with mucus due to pressure gradients associated with the flat PCL/mucus interface. This phenomenon complements previously known mechanisms for ciliary propulsion. Modelling results are related to clinical findings, in particular the increased MCC observed in patients with pseudohypoaldosteronism. Recent important advances by several groups in modelling the fluid–structure interaction by which the cilia movement and fluid transport emerge from specification of internal mechanics, viscous and elastic forces are reviewed. Finally, we discuss the limitations of existing work, and the challenges for the next generation of models, which may provide further insight into this complex and vital system

Role of Homer Proteins in the Maintenance of Sleep-Wake States

Sleep is an evolutionarily conserved process that is linked to diurnal cycles and normal daytime wakefulness. Healthy sleep and wakefulness are integral to a healthy lifestyle; this occurs when an organism is able to maintain long bouts of both sleep and wake. Homer proteins, which function as adaptors for group 1 metabotropic glutamate receptors, have been implicated in genetic studies of sleep in both Drosophila and mouse. Drosophila express a single Homer gene product that is upregulated during sleep. By contrast, vertebrates express Homer as both constitutive and immediate early gene (H1a) forms, and H1a is up-regulated during wakefulness. Genetic deletion of Homer in Drosophila results in fragmented sleep and in failure to sustain long bouts of sleep, even under increased sleep drive. However, deletion of Homer1a in mouse results in failure to sustain long bouts of wakefulness. Further evidence for the role of Homer1a in the maintenance of wake comes from the CREB alpha delta mutant mouse, which displays a reduced wake phenotype similar to the Homer1a knockout and fails to up-regulate Homer1a upon sleep loss. Homer1a is a gene whose expression is induced by CREB. Sustained behaviors of the sleep/wake cycle are created by molecular pathways that are distinct from those for arousal or short bouts, and implicate an evolutionarily-conserved role for Homer in sustaining these behaviors

Retire statistical significance

Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects

Count Models Based on Weibull Interarrival Times

The widespread popularity and use of both the Poisson and the negative binomial models for count data arise, in part, from their derivation as the number of arrivals in a given time period assuming exponentially distributed interarrival times (without and with heterogeneity in the underlying base rates, respectively). However, with that clean theory come some limitations including limited flexibility in the assumed underlying arrival rate distribution and the inability to model underdispersed counts (variance less than the mean). Although extant research has addressed some of these issues, there still remain numerous valuable extensions. In this research, we present a model that, due to computational tractability, was previously thought to be infeasible. In particular, we introduce here a generalized model for count data based upon an assumed Weibull interarrival process that nests the Poisson and negative binomial models as special cases. The computational intractability is overcome by deriving the Weibull count model using a polynomial expansion which then allows for closed-form inference (integration term-by-term) when incorporating heterogeneity due to the conjugacy of the expansion and a commonly employed gamma distribution. In addition, we demonstrate that this new Weibull count model can (1) model both over- and underdispersed count data, (2) allow covariates to be introduced in a straightforward manner through the hazard function, and (3) be computed in standard software.

Comparison of sleep wake data in Homer1a knockout, Homer1a heterozygote and wildtype littermate mice during the lights off and lights on periods.

<p>Averages± standard deviations shown. Values are given for the average plus/minus standard deviation. There were significant interactions between genotype and time of day for amounts of wake (p = 0.0002), NREM sleep (p = 0.001) and REM sleep (p = 0.002). ^asignificantly different from wildtype p<0.05, p>0.01; ^bsignificantly different from wildtype p<0.001; ^csignificantly different from wildtype p<0.0001; ^dsignificantly different from Homer1a heterozygote p<0.05, p>0.01; ^esignificantly different from Homer1a heterozygote p<0.001; ^fsignificantly different from Homer1a heterozygote p<0.0001.</p

Loss of Drosophila Homer alters sleep architecture.

<p>A) Representative actograms displaying activity in <i>homer</i>^R102 (right panel) and CS (left panel) flies under a 12∶12 L:D regimen. Bar at the top denotes light and dark periods. B) The average number of sleep bouts and standard deviation during the day and night in Homer^R102 (gray bar; n = 47) and the wildtype CS flies (black bar; n = 65) as determined by video analysis. The average number of sleep bouts are significantly greater in homer flies (*p<0.0001). C) Histogram showing the average sleep bout duration and standard deviation during the day and night in Homer^R102 (gray bar; n = 47) and the wildtype CS flies (black bar; n = 65) as determined by video analysis. The average sleep bout is significantly shorter in homer flies (*p<0.0001). This difference is particularly marked at night.</p