In Sweden, reporting is voluntary for most common felled game, and the number of voluntary reports can vary between hunting teams, HMP, and counties. In 2020, an improved harvest estimation model was developed, which reduced the sensitivity to low reporting. However, there were still some limits to the model, where large, credible intervals were estimated. Therefore, additional variables were considered as the model does not take into account landcover among HMPs, [2] the impact of climate, [4] wildlife accidents, and [4] geographical distribution, creating the covariate model. This study aimed to compare the new model with the covariate model to see if covariates would reduce the large, credible intervals. Two hypothesis tests were performed: evaluation of predictive performance using leave one out cross-validation and evaluation of the 95 % credible interval. Evaluation of predictive performance was performed by examining the difference in expected log-pointwise predictive density (ELPD) and standard error (SE) for each species and model. The results show that the covariates model ranked highest for all ten species, and out of the ten species, six had an (ELPD) difference of two to four, which implies that there is support that the covariate model will be a better predictor for other datasets than this one. At least one covariate had an apparent effect on harvest estimates for nine out of ten species. Finally, the covariate model reduced the large uncertainties, which was an improvement of the null model, indicating that harvest estimates can be improved by taking covariates into account.

<div><div><div><p>Structural identification is a useful tool for detecting damage and damage evolution in a structure. The initiation of damage in a structure and its subsequent growth are mainly associated with nonlinear behaviors. While linear dynamics of a structure are easy to simulate, nonlinear structural dynamics have more complex dynamics and amplitude dependence that do require more sophisticated simulation tools and identification methods compared to linear systems. Additionally, there are generally many more parameters in nonlinear models and the responses may not be sensitive to all of them for all inputs. To develop model selection methods, an experiment is conducted that uses an existing device with repeatable behavior and having an expected model from the literature. In this case, an MR damper is selected as the experimental device. The objective of this research is to develop and demonstrate a method to select the most appropriate model from a set of identified stochastic models of a nonlinear device. The method is developed using numerical example of a common nonlinear system, and is then implemented on an experimental structural system with unknown nonlinear properties. Bayesian methods are used because they provide a distinct advantage over many other existing methods due to their ability to provide confidence on answers given the observed data and initial uncertainty. These methods generate a description of the parameters of the system given a set of observations. First, the selected model of the MR damper is simulated and used for demonstrating the results on a numerical example. Second, the model selection process is demonstrated on an experimental structure based on experimental data. This study explores the use of the Bayesian approach for nonlinear structural identification and identifies a number of lessons for others aiming to employ Bayesian inference.</p></div></div></div>

While time-dependent processes are important to biological functions, methods to leverage temporal information from large data have remained computationally challenging. In temporal gene-expression data, clustering can be used to identify genes with shared function in complex processes. Algorithms like K-Means and standard Gaussian mixture-models (GMM) fail to account for variability in replicated data or repeated measures over time and require a priori cluster number assumptions, evaluating many cluster numbers to select an optimal result. An improved penalized-GMM offers a computationally-efficient algorithm to simultaneously optimize cluster number and labels. The work presented in this dissertation was motivated by mice bone-fracture models interested in determining patterns of temporal gene-expression during bone-healing progression. To solve this, an extension to the penalized-GMM was proposed to account for correlation between replicated data and repeated measures over time by introducing random-effects using a mixture of mixed-effects polynomial regression models and an entropy-penalized EM-Algorithm (EPEM). First, performance of EPEM for different mixed-effects models were assessed with simulation studies and applied to the fracture-healing study. Second, modifications to address the high computational cost of EPEM were considered that either clustered subsets of data determined by predicted polynomial-order (S-EPEM) or used modified-initialization to decrease the initial burden (I-EPEM). Each was compared to EPEM and applied to the fracture-healing study. Lastly, as varied rates of fracture-healing were observed for mice with different genetic-backgrounds (strains), a new analysis strategy was proposed to compare patterns of temporal gene-expression between different mice-strains and assessed with simulation studies. Expression-profiles for each strain were treated as separate objects to cluster in order to determine genes clustered into different groups across strain. We found that the addition of random-effects decreased accuracy of predicted cluster labels compared to K-Means, GMM, and fixed-effects EPEM. Polynomial-order optimization with BIC performed with highest accuracy, and optimization on subspaces obtained with singular-value-decomposition performed well. Computation time for S-EPEM was much reduced with a slight decrease in accuracy. I-EPEM was comparable to EPEM with similar accuracy and decrease in computation time. Application of the new analysis strategy on fracture-healing data identified several distinct temporal gene-expression patterns for the different strains. / 2021-02-27T00:00:00Z

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The linear mixed model is widely implemented in the analysis of longitudinal data. Inference techniques and information criteria are available and well-studied for goodness-of-fit within the linear mixed model setting. Predictive approaches such as R-squared, PRESS, and CCC are available for the linear mixed model but require more research (Edward, 2005). This project used simulation to investigate the performance of R-squared, PRESS, CCC, Pseudo F-test and information criterion for goodness-of-fit within the linear mixed model framework. Marginal and conditional approaches for these predictive statistics were studied under different variance-covariance structures. For compound symmetry structure, the success rates for all 17 statistics (marginal and conditional R-squared, PRESS, CCC, F test, AIC and BIC) were high. The study suggested using marginal rather than conditional residuals for PRESS, CCC and R-squared. It suggested using REML likelihood function which has the determinant term for AIC and BIC. For CCC, R-squared, and the information criterion, there was no difference for the various parameter number adjustments. For autoregressive order 1 plus random effect, the study suggested using conditional residuals for PRESS, marginal residuals for CCC and R-squared, and using REML function with the determinant term for AIC and BIC. Also there was no difference for the different parameter number adjustments. The F-test performed well for all covariance structures. The study also indicated that characteristics of the data, such as the covariance structure, parameter values, and sample size, can greatly impact performance of various statistics. No one criterion is consistently better than the others in terms of selection performance in the simulation study.

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The advancement in data acquiring technology continues to see survival data sets with many covariates. This has posed a new challenge for researchers in identifying important covariates for inference and prediction for a time-to-event response variable. In this dissertation, common Cox proportional hazards model selection techniques and a random survival forest technique were compared using five performance criteria measures. These performance measures were concordance index, integrated area under the curve, and , and R2 . To carry out this exercise, a multicentre clinical trial data set was used. A simulation study was also implemented for this comparison. To develop a Cox proportional model, a training dataset of 75% of the observations was used and the model selection techniques were implemented to select covariates. Full Cox PH models containing all covariates were also incorporated for analysis for both the clinical trial data set and simulations. The clinical trial data set showed that the full model and forward selection technique performed better with the performance metrics employed, though they do not reduce the complexity of the model as much as the Lasso technique does. The simulation studies also showed that the full model performed better than the other techniques, with the Lasso technique overpenalising the model from the simulation with the smaller data set and many covariates. AIC and BIC were less effective in computation than the rest of the variable selection techniques, but effectively reduced model complexity than their counterparts for the simulations. The integrated area under the curve was the performance metric of choice for choosing the final model for analysis on the real data set. This performance metric gave more efficient outcomes unlike the other metrics on all selection techniques. This dissertation hence showed that variable selection techniques differ according to the study design of the research as well as the performance measure used. Hence, to have a good model, it is important to not use a model selection technique in isolation. There is therefore need for further research and publish techniques that work generally well for different study designs to make the process shorter for most researchers.

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