PSI-17 Genetic analysis of weaning weight in Hungarian Simmental cattle

The aim of the study was to partition the total phenotypic variation in the weaning weight of Hungarian Simmental calves into their various causal components. The data used was provided by the Association of Hungarian Simmental Breeders. The dataset comprised of the weaning weight records of 44,278 calves (sire = 879, dam = 14,811) born from 1975 to 2020. A total of six models were fitted to the weaning weight data. Herd, birth year, calving order and sex were included as fixed effects in the models. Model 1 had direct genetic effect as the only random effect. Model 2 had a permanent maternal environment as an additional random effect. Model 3 had both direct and maternal genetic effects, with their covariance is being zero. Model 4 was similar to Model 3 but with non-zero direct-maternal genetic covariance. Model 5 had direct, maternal genetic and permanent environmental effects and a zero direct-maternal genetic covariance. Model 6 was similar to model 5 but the direct-maternal genetic effect was assumed to be correlated. Variance components and genetic parameters were estimated using restricted maximum likelihood method with the Wombat software. The best fit model was determined using the Log likelihood ratio test. Inclusion of direct maternal genetic covariance increased the variance components estimates dramatically which resulted in a corresponding increase in the direct and maternal heritability estimates. The best fitted model (Model 4) had direct and maternal genetic effects as the only random effects with a non-zero direct-maternal genetic covariance. The direct heritability, maternal heritability and direct-maternal genetic correlation estimate of the best model was 0.57, 0.16 and -0.78, respectively. Our result suggests the problem of (co)sampling variation in the partitioning of additive genetic effect into direct and maternal components.

SymPKF (v1.0): a symbolic and computational toolbox for the design of parametric Kalman filter dynamics

Recent research in data assimilation has led to the introduction of the parametric Kalman filter (PKF): an implementation of the Kalman filter, whereby the covariance matrices are approximated by a parameterized covariance model. In the PKF, the dynamics of the covariance during the forecast step rely on the prediction of the covariance parameters. Hence, the design of the parameter dynamics is crucial, while it can be tedious to do this by hand. This contribution introduces a Python package, SymPKF, able to compute PKF dynamics for univariate statistics and when the covariance model is parameterized from the variance and the local anisotropy of the correlations. The ability of SymPKF to produce the PKF dynamics is shown on a nonlinear diffusive advection (the Burgers equation) over a 1D domain and the linear advection over a 2D domain. The computation of the PKF dynamics is performed at a symbolic level, but an automatic code generator is also introduced to perform numerical simulations. A final multivariate example illustrates the potential of SymPKF to go beyond the univariate case.

Dark energy survey year 3 results: Covariance modelling and its impact on parameter estimation and quality of fit

We describe and test the fiducial covariance matrix model for the combined 2-point function analysis of the Dark Energy Survey Year 3 (DES-Y3) dataset. Using a variety of new ansatzes for covariance modelling and testing we validate the assumptions and approximations of this model. These include the assumption of Gaussian likelihood, the trispectrum contribution to the covariance, the impact of evaluating the model at a wrong set of parameters, the impact of masking and survey geometry, deviations from Poissonian shot-noise, galaxy weighting schemes and other, sub-dominant effects. We find that our covariance model is robust and that its approximations have little impact on goodness-of-fit and parameter estimation. The largest impact on best-fit figure-of-merit arises from the so-called fsky approximation for dealing with finite survey area, which on average increases the χ2 between maximum posterior model and measurement by $3.7{{\ \rm per\ cent}}$ (Δχ2 ≈ 18.9). Standard methods to go beyond this approximation fail for DES-Y3, but we derive an approximate scheme to deal with these features. For parameter estimation, our ignorance of the exact parameters at which to evaluate our covariance model causes the dominant effect. We find that it increases the scatter of maximum posterior values for Ωm and σ8 by about $3{{\ \rm per\ cent}}$ and for the dark energy equation of state parameter by about $5{{\ \rm per\ cent}}$.

Discussion on Competition for Spatial Statistics for Large Datasets

We discuss the experiences and results of the AppStatUZH team’s participation in the comprehensive and unbiased comparison of different spatial approximations conducted in the Competition for Spatial Statistics for Large Datasets. In each of the different sub-competitions, we estimated parameters of the covariance model based on a likelihood function and predicted missing observations with simple kriging. We approximated the covariance model either with covariance tapering or a compactly supported Wendland covariance function.

Optimal model for weaning-weight of Bunaji Bulls at NAPRI Farm, Shika, Nigeria

Empirical models have over the years been commonly established by animal research centers for the study of weight-age profiles in order to understand the metabolic processes of growth. They provide efficient parameter estimates for mature weight and rate of maturing, but were found to consistently either over-or-under estimate the mature weight. estimate the mature weight. They also perform poorly in predicting weight in early life or beyond the range of input data. At the National Animal Production Research Institute (NAPRI) farm, Shika, Brody was established as the model that provides efficient parameter estimates of weight-age profiles for Bunaji bulls. However, a major drawback of the model is its consistent underestimation of weight prior to six months of age, leading to poor prediction of weaning weight. To address this shortcoming, we propose in this article a joint mean-covariance model that provide optimal parameter estimates for the weaning weight of Bunaji bulls

Measuring psychological pain: psychometric analysis of the Orbach and Mikulincer Mental Pain Scale

Background Suicide is a public health concern, with an estimated 1 million individuals dying each year worldwide. Individual psychological pain is believed to be a contributing motivating factor. Therefore, establishing a psychometrically sound tool to adequately measure psychological pain is important. The Orbach and Mikulincer Mental Pain Scale (OMMP) has been proposed; however, previous psychometric analysis on the OMMP has not yielded a consistent scale structure, and the internal consistency of the subscales has not met recommended values. Therefore, the primary purpose of this study was to assess the psychometric properties of the OMMP in a diverse sample. Methods A confirmatory factor analysis (CFA) on the 9-factor, 44-item OMMP was conducted on the full sample (n = 1151). Because model fit indices were not met, an exploratory factor analysis (EFA) was conducted on a random subset of the data (n = 576) to identify a more parsimonious structure. The EFA structure was then tested in a covariance model in the remaining subset of participants (n = 575). Multigroup invariance testing was subsequently performed to examine psychometric properties of the refined scale. Results The CFA of the original 9-factor, 44-item OMMP did not meet recommended model fit recommendations. The EFA analysis results revealed a 3-factor, 9-item scale (i.e., OMMP-9). The covariance model of the OMMP-9 indicated further refinement was necessary. Multigroup invariance testing conducted on the final 3-factor, 8-item scale (i.e., OMMP-8) across mental health diagnoses, sex, injury status, age, activity level, and athlete classification met all criteria for invariance. Conclusions The 9-factor, 44-item OMMP does not meet recommended measurement criteria and should not be recommended for use in research and clinical practice in its current form. The refined OMMP-8 may be a more viable option to use; however, more research should be completed prior to adoption.

SymPKF (v1.0): a symbolic and computational toolbox for the design of parametric Kalman filter dynamics

Recent researches in data assimilation lead to the introduction of the parametric Kalman filter (PKF): an implementation of the Kalman filter, where the covariance matrices are approximated by a parameterized covariance model. In the PKF, the dynamics of the covariance during the forecast step relies on the prediction of the covariance parameters. Hence, the design of the parameter dynamics is crucial while it can be tedious to do this by hand. This contribution introduces a python package, SymPKF, able to compute PKF dynamics for univariate statistics and when the covariance model is parameterized from the variance and the local anisotropy of the correlations. The ability of SymPKF to produce the PKF dynamics is shown on a non-linear diffusive advection (Burgers equation) over a 1D domain and the linear advection over a 2D domain. The computation of the PKF dynamics is performed at a symbolic level, but an automatic code generator is also introduced to perform numerical simulations. A final multivariate example illustrates the potential of SymPKF to go beyond the univariate case.