Standard Discrete Choice Models (DCMs) assume that unobserved effects that influence decision- making are independently and identically distributed among individuals. When unobserved effects are spatially correlated, ...
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Standard Discrete Choice Models (DCMs) assume that unobserved effects that influence decision- making are independently and identically distributed among individuals. When unobserved effects are spatially correlated, the independence assumption does not hold, leading to biased standard errors and potentially biased parameter estimates. This paper proposes an interpretable hierarchical Nearest Neighbor gaussianprocess (HNNGP) model to account for spatially correlated unobservablesin discrete choice analysis. gaussianprocesses (GPs) are often regarded as lacking interpretability due to their non-parametric nature. However, we demonstrate how to incorporate GPs directly into the latent utility specification to flexibly model spatially correlated unobserved effects without sacrificing structural economic interpretation. To empirically test our proposed HNNGP models, we analyze binary and multinomial mode choices for commuting to work in New York City. For the multinomial case, we formulate and estimate HNNGPs with and without independence from irrelevant alternatives (IIA). Building on the interpretability of our modeling strategy, we provide both point estimates and credible intervals for the value of travel time savings in NYC. Finally, we compare the results from all proposed specifications with those derived from a standard logit model and a probit model with spatially autocorrelated errors (SAE) to showcase how accounting for different sources of spatial correlation in discrete choice can significantly impact inference. We also show that the HNNGP models attain better out-of- sample prediction performance when compared to the logit and probit SAE models, especially in the multinomial case.
Dissolution studies are an integral part of pharmaceutical drug development, yet standard methods for analysing dissolution data are inadequate for capturing the true underlying shapes of the dissolution curves. Metho...
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Dissolution studies are an integral part of pharmaceutical drug development, yet standard methods for analysing dissolution data are inadequate for capturing the true underlying shapes of the dissolution curves. Methods based on similarity factors, such as the f2 statistic, have been developed to demonstrate comparability of dissolution curves, however, this inability to capture the shapes of the dissolution curves can lead to substantial bias in comparability estimators. In this article, we propose two novel semi-parametric dissolution curve modeling strategies for establishing the comparability of dissolution curves. The first method relies upon hierarchical gaussian process regression models to construct an f2 statistic based on continuous time modeling that results in significant bias reduction. The second method uses a Bayesian model selection approach for creating a framework that does not suffer from the limitations of the f2 statistic. Overall, these two methods are shown to be superior to their comparator methods and provide feasible alternatives for similarity assessment under practical limitations. Illustrations highlighting the success of our methods are provided for two motivating real dissolution data sets from the literature, as well as extensive simulation studies.
With the improvement of the accuracy of numerical simulation experiments, the computational costs of black-box function problems are increasing. In the process of global optimization using the Bayesian optimization me...
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With the improvement of the accuracy of numerical simulation experiments, the computational costs of black-box function problems are increasing. In the process of global optimization using the Bayesian optimization method, the sampling cost and the optimization accuracy are the keys for measuring the effectiveness of the Bayesian optimization method. The expected improvement method has a closed-form of the acquisition function, which can realize the effective use of the sampled points, thereby reducing the sampling cost and improving the optimization accuracy. Therefore, this method has been widely used to solve practical engineering problems. The current expected improvement methods are based on fixed local-global search strategy. These methods have difficulty adapting to black-box functions of different complexities, and they have significant limitations in balancing local and global search capabilities. This article proposes a generalized hierarchical expected improvement (GHEI) Bayesian optimization method with an adaptive search strategy. By introducing the balance parameters to adjust the improvement functions, the local-global search criterion is further changed. On this basis, an adaptive search strategy based on the equivalent expectation to improve the optimization is proposed, and it improves the ability to deal with black-box functions of different complexities. The accuracy of the hierarchical gaussian process model is further improved through methods such as related parameter estimation, hyperparameter determination, and basis function order selection. Comparative analysis with numerical calculation examples verified the effectiveness of the proposed method. Finally, the adaptive GHEI method is applied on the spacecraft radiation resistance equivalent test analysis, and results show that the adaptive GHEI method exhibits a strong search efficiency and applicability .(c) 2022 Published by Elsevier Inc.
Computational modeling of the heart has contributed tremendously in quantitatively understanding the cardiac functions, showing great potential to assist medical doctors in heart-disease diagnosis. However, cardiac si...
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Computational modeling of the heart has contributed tremendously in quantitatively understanding the cardiac functions, showing great potential to assist medical doctors in heart-disease diagnosis. However, cardiac simulation is generally subject to uncertainties and variabilities among different individuals. Traditional "one-size-fits-all" simulation is limited in providing individualized optimal diagnosis and treatment for patients with heart disease. Realizing the full potential of cardiac computational modeling in clinical practice requires effective and efficient model personalization. In this paper, we develop a spatiotemporal modeling and optimization framework for cardiac model calibration. The proposed calibration framework not only effectively quantifies the spatiotemporal discrepancy between the simulation model and the physical cardiac system, but also increases the computational efficiency in personalized modeling of cardiac electrophysiology. The model performance is validated and evaluated in the 3D cardiac simulation. Numerical experiments demonstrate that the proposed framework significantly outperforms traditional approaches in calibrating the cardiac simulation.
BackgroundFor brown bears (Ursus arctos), hibernation is a critical part of the annual life cycle because energy savings during hibernation can be crucial for overwintering, and females give birth to cubs at that time...
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BackgroundFor brown bears (Ursus arctos), hibernation is a critical part of the annual life cycle because energy savings during hibernation can be crucial for overwintering, and females give birth to cubs at that time. For hibernation to be a useful strategy, timing is critical. However, environmental conditions vary greatly, which might have a negative effect on the functionality of the evolved biological time-keeping. Here, we used a long-term dataset (69years) on brown bear denning phenology recorded in 12 Russian protected areas and quantified the phenological responses to variation in temperature and snow depth. Previous studies analyzing the relationship between climate and denning behavior did not consider that the brown bear response to variation in climatic factors might vary through a period preceding den entry and exit. We hypothesized that there is a seasonal sensitivity pattern of bear denning phenology in response to variation in climatic conditions, such that the effect of climatic variability will be pronounced only when it occurs close to den exit and entry *** found that brown bears are most sensitive to climatic variations around the observed first den exit and last entry dates, such that an increase/decrease in temperature in the periods closer to the first den exit and last entry dates have a greater influence on the denning dates than in other *** study shows that climatic factors are modulating brown bear hibernation phenology and provide a further structuring of this modulation. The sensitivity of brown bears to changes in climatic factors during hibernation might affect their ability to cope with global climate change. Therefore, understanding these processes will be essential for informed management of biodiversity in a changing world.
In many applications of functional data analysis, summarising functional variation based on fits, without taking account of the estimation process, runs the risk of attributing the estimation variation to the function...
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In many applications of functional data analysis, summarising functional variation based on fits, without taking account of the estimation process, runs the risk of attributing the estimation variation to the functional variation, thereby overstating the latter. For example, the first eigenvalue of a sample covariance matrix computed from estimated functions may be biased upwards. We display a set of estimated neuronal Poisson-process intensity functions where this bias is substantial, and we discuss two methods for accounting for estimation variation. One method uses a random-coefficient model, which requires all functions to be fitted with the same basis functions. An alternative method removes the same-basis restriction by means of a hierarchical gaussian process model. In a small simulation study the hierarchical gaussian process model outperformed the random-coefficient model and greatly reduced the bias in the estimated first eigenvalue that would result from ignoring estimation variability. For the neuronal data the hierarchical gaussian process estimate of the first eigenvalue was much smaller than the naive estimate that ignored variability due to function estimation. The neuronal setting also illustrates the benefit of incorporating alignment parameters into the hierarchical scheme.
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