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Longitudinal studies with binary or ordinal responses are widely encountered in various disciplines, where the primary focus is on the temporal evolution of the probability of each response category. Traditional approaches build from the generalized mixed effects modeling framework. Even amplified with nonparametric priors placed on the fixed or random effects, such models are restrictive due to the implied assumptions on the marginal expectation and covariance structure of the responses. We tackle the problem from a functional data analysis perspective, treating the observations for each subject as realizations from subject-specific stochastic processes at the measured times. We develop the methodology focusing initially on binary responses, for which we assume the stochastic processes have Binomial marginal distributions. Leveraging the logits representation, we model the discrete space processes through continuous space processes. We utilize a hierarchical framework to model the mean and covariance kernel of the continuous space processes nonparametrically and simultaneously through a Gaussian process prior and an Inverse-Wishart process prior, respectively. The prior structure results in flexible inference for the evolution and correlation of binary responses, while allowing for borrowing of strength across all subjects. The modeling approach can be naturally extended to ordinal responses. Here, the continuation-ratio logits factorization of the multinomial distribution is key for efficient modeling and inference, including a practical way of dealing with unbalanced longitudinal data. The methodology is illustrated with synthetic data examples and an analysis of college students’ mental health status data. [ABSTRACT FROM AUTHOR]

We develop a prior probability model for temporal Poisson process intensities through structured mixtures of Erlang densities with common scale parameter, mixing on the integer shape parameters. The mixture weights are constructed through increments of a cumulative intensity function which is modeled nonparametrically with a gamma process prior. Such model specification provides a novel extension of Erlang mixtures for density estimation to the intensity estimation setting. The prior model structure supports general shapes for the point process intensity function, and it also enables effective handling of the Poisson process likelihood normalizing term resulting in efficient posterior simulation. The Erlang mixture modeling approach is further elaborated to develop an inference method for spatial Poisson processes. The methodology is examined relative to existing Bayesian nonparametric modeling approaches, including empirical comparison with Gaussian process prior based models, and is illustrated with synthetic and real data examples. [ABSTRACT FROM AUTHOR]

We develop two prior distributions for probability vectors which, in contrast to the popular Dirichlet distribution, retain sparsity properties in the presence of data. Our models are appropriate for count data with many categories, most of which are expected to have negligible probability. Both models are tractable, allowing for efficient posterior sampling and marginalization. Consequently, they can replace the Dirichlet prior in hierarchical models without sacrificing convenient Gibbs sampling schemes. We derive both models and demonstrate their properties. We then illustrate their use for model-based selection with a hierarchical model in which we infer the active lag from time-series data. Using a squared-error loss, we demonstrate the utility of the models for data simulated from a nearly deterministic dynamical system. We also apply the prior models to an ecological time series of Chinook salmon abundance, demonstrating their ability to extract insights into the lag dependence. [ABSTRACT FROM AUTHOR]

This is an invited discussion of review paper “Nonparametric Bayesian Inference in Applications” by Peter Müller, Fernando A. Quintana and Garritt L. Page. [ABSTRACT FROM AUTHOR]

Integro-difference equations (IDEs) provide a flexible framework for dynamic modeling of spatio-temporal data. The choice of kernel in an IDE model relates directly to the underlying physical process modeled, and it can affect model fit and predictive accuracy. We introduce Bayesian non-parametric methods to the IDE literature as a means to allow flexibility in modeling the kernel. We propose a mixture of normal distributions for the IDE kernel, built from a spatial Dirichlet process for the mixing distribution, which can model kernels with shapes that change with location. This allows the IDE model to capture non-stationarity with respect to location and to reflect a changing physical process across the domain. We address computational concerns for inference that leverage the use of Hermite polynomials as a basis for the representation of the process and the IDE kernel, and incorporate Hamiltonian Markov chain Monte Carlo steps in the posterior simulation method. An example with synthetic data demonstrates that the model can successfully capture location-dependent dynamics. Moreover, using a data set of ozone pressure, we show that the spatial Dirichlet process mixture model outperforms several alternative models for the IDE kernel, including the state of the art in the IDE literature, that is, a Gaussian kernel with location-dependent parameters. [ABSTRACT FROM AUTHOR]

We present a Bayesian nonparametric modeling approach to inference and risk assessment for developmental toxicity studies. The primary objective of these studies is to determine the relationship between the level of exposure to a toxic chemical and the probability of a physiological or biochemical response. We consider a general data setting involving clustered categorical responses on the number of prenatal deaths, the number of live pups, and the number of live malformed pups from each laboratory animal, as well as continuous outcomes (e.g., body weight) on each of the live pups. We utilize mixture modeling to provide flexibility in the functional form of both the multivariate response distribution and the various dose-response curves of interest. The nonparametric model is built from a structured mixture kernel and a dose-dependent Dirichlet process prior for the mixing distribution. The modeling framework enables general inference for the implied dose-response relationships and for dose-dependent correlations between the different endpoints, features which provide practical advances relative to traditional parametric models for developmental toxicology. We use data from a toxicity experiment that investigated the toxic effects of an organic solvent (diethylene glycol dimethyl ether) to demonstrate the range of inferences obtained from the nonparametric mixture model, including comparison with a parametric hierarchical model. Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

Stationary time series models built from parametric distributions are, in general, limited in scope due to the assumptions imposed on the residual distribution and autoregression relationship. We present a modeling approach for univariate time series data, which makes no assumptions of stationarity, and can accommodate complex dynamics and capture non-standard distributions. The model for the transition density arises from the conditional distribution implied by a Bayesian nonparametric mixture of bivariate normals. This results in a flexible autoregressive form for the conditional transition density, defining a time-homogeneous, non-stationary Markovian model for real-valued data indexed in discrete time. To obtain a computationally tractable algorithm for posterior inference, we utilize a square-root-free Cholesky decomposition of the mixture kernel covariance matrix. Results from simulated data suggest that the model is able to recover challenging transition densities and non-linear dynamic relationships. We also illustrate the model on time intervals between eruptions of the Old Faithful geyser. Extensions to accommodate higher order structure and to develop a state-space model are also discussed. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the intrinsic parameters of the 3D Velodyne lidar while at the same time computing its extrinsic calibration with respect to a rigidly connected camera. Existing approaches to solve this nonlinear estimation problem are based on iterative minimization of nonlinear cost functions. In such cases, the accuracy of the resulting solution hinges on the availability of a precise initial estimate, which is often not available. In order to address this issue, we divide the problem into two least-squares sub-problems, and analytically solve each one to determine a precise initial estimate for the unknown parameters. We further increase the accuracy of these initial estimates by iteratively minimizing a batch nonlinear least-squares cost function. In addition, we provide the minimal observability conditions, under which, it is possible to accurately estimate the unknown parameters. Experimental results consisting of photorealistic 3D reconstruction of indoor and outdoor scenes are used to assess the validity of our approach. [ABSTRACT FROM AUTHOR]

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Background and Objectives: Statins (HMGCoA-reductase inhibitors) produce numerous non-lipid related, 'pleiotropic' effects. Our aim was to investigate whether simvastatin treatment affects serum levels of vascular calcification inhibitors, such as fetuin-A, osteoprotegerin (OPG) and osteopontin (OPN), in patients with coronary artery disease (CAD). Methods: A total of 98 statin-free patients with angiographically proven, newly diagnosed CAD were treated with simvastatin (20-40 mg daily) for 6 months to target a low-density lipoprotein (LDL) level <100 mg/dL (the statin group [SG]). Thirty-five age- and sex-matched healthy individuals without any chronic metabolic or cardiovascular disease at baseline served as a healthy control group (HCG). Clinical, anthropometrical and metabolic parameters and serum fetuin-A, OPG, OPN and high-sensitivity C-reactive protein (hsCRP) levels were assayed at baseline in all participants and after 6 months only in SG patients. Results: Compared with HCG subjects at baseline, SG patients exhibited higher serum levels of OPG (7.39 ± 2.94 pmol/L vs 2.47 ± 1.15 pmol/L, p < 0.001), OPN (60.99 ± 17.52 ng/mL vs 45.45 ± 10.26 ng/mL, p = 0.005) and hsCRP (4.66 ± 1.74 mg/L vs 1.58 ± 0.56 mg/L, p < 0.001) as well as lower serum levels of fetuin-A (0.222 ± 0.036 μg/L vs 0.839 ± 0.092 μg/L, p < 0001). Apart from significantly reducing plasma total cholesterol and LDL, simvastatin also reduced serum levels of fetuin-A (by ~62.6 %), OPG (by ~47.2 %), OPN (by ~44.6 %) and hsCRP (by ~45.3 %) ( p < 0.05) in SG patients. In standard multiple regression analysis, the simvastatin-induced reduction in fetuin-A was independently associated with changes in total cholesterol (β = −0.289, p = 0.048) and LDL ( β = −0.302, p = 0.032) ( R = 0.305, p = 0.040). Conclusion: Patients with CAD showed derangements in serum levels of all vascular calcification inhibitors compared with those in healthy controls. Simvastatin treatment for 6 months significantly decreased serum fetuin-A, OPG and OPN levels, but the clinical relevance of this requires further investigation. [ABSTRACT FROM AUTHOR]

Marsupial robot teams offer the ability to augment or replace human-based responses to hazardous scenarios such as search-and-rescue missions or monitoring toxic environments. Coordination and longevity of the members of the marsupial robotic team can quickly become burdensome as the scope of the scenario changes. To facilitate large scale operations, robotic teams must be able to function continuously with limited to no human intervention for scenarios which may not have a pre-determined length at onset. An end-to-end design and implementation of a novel marsupial system where a multi-level hierarchy allows larger robots to transport, deploy, coordinate, recover, and resupply large numbers of smaller deployable systems is outlined. The design and implementation of hardware systems for performing these actions is discussed. In addition, the algorithms which coordinate the team members are presented. Simulation illustrating the scalability of the approach is presented including experiments illustrating a light-weight vision-based autonomous docking capability. [ABSTRACT FROM AUTHOR]

The stock and recruitment relationship is fundamental to the management of fishery natural resources. However, inferring stock-recruitment relationships is a challenging problem because of the limited available data, the collection of plausible models, and the biological characteristics that should be reflected in the model. Motivated by limitations of traditional parametric stock-recruitment models, we propose a Bayesian nonparametric approach based on a mixture model for the joint distribution of log-reproductive success and stock biomass. Flexible mixture modeling for this bivariate distribution yields rich inference for the stock-recruitment relationship through the implied conditional distribution of log-reproductive success given stock biomass. The method is illustrated with cod data from six regions of the North Atlantic, including comparison with simpler Bayesian parametric and semiparametric models. [ABSTRACT FROM AUTHOR]

We propose a semiparametric modeling approach for mixtures of symmetric distributions. The mixture model is built from a common symmetric density with different components arising through different location parameters. This structure ensures identifiability for mixture components, which is a key feature of the model as it allows applications to settings where primary interest is inference for the subpopulations comprising the mixture. We focus on the two-component mixture setting and develop a Bayesian model using parametric priors for the location parameters and for the mixture proportion, and a nonparametric prior probability model, based on Dirichlet process mixtures, for the random symmetric density. We present an approach to inference using Markov chain Monte Carlo posterior simulation. The performance of the model is studied with a simulation experiment and through analysis of a rainfall precipitation data set as well as with data on eruptions of the Old Faithful geyser. [ABSTRACT FROM AUTHOR]

We propose a prior probability model for two distributions that are ordered according to a stochastic precedence constraint, a weaker restriction than the more commonly utilized stochastic order constraint. The modeling approach is based on structured Dirichlet process mixtures of normal distributions. Full inference for functionals of the stochastic precedence constrained mixture distributions is obtained through a Markov chain Monte Carlo posterior simulation method. A motivating application involves study of the discriminatory ability of continuous diagnostic tests in epidemiologic research. Here, stochastic precedence provides a natural restriction for the distributions of test scores corresponding to the non-infected and infected groups. Inference under the model is illustrated with data from a diagnostic test for Johne's disease in dairy cattle. We also apply the methodology to the comparison of survival distributions associated with two distinct conditions, and illustrate with analysis of data on survival time after bone marrow transplantation for treatment of leukemia. [ABSTRACT FROM AUTHOR]

In comparing two populations, sometimes a model incorporating a certain probability order is desired. In this selling, Bayesian modeling is attractive since a probability order restriction imposed a priori on the population distributions is retained a posteriori. Extending the work in Gelfand and Kottas (2001) for stochastic order specifications, we formulate modeling for distributions ordered in variability. We work with Dirichlet process mixtures resulting in a fully Bayesian semiparametric approach. The details for simulation-based model fitting and prior specification are provided. An example, based on two small subsets of time intervals between eruptions of the Old Faithful geyser, illustrates the methodology. [ABSTRACT FROM AUTHOR]

In comparing two populations, sometimes a model incorporating stochastic order is desired. Customarily, such modeling is done parametrically. The objective of this paper is to formulate nonparametric (possibly semiparametric) stochastic order specifications providing richer, more flexible modeling. We adopt a fully Bayesian approach using Dirichlet process mixing. An attractive feature of the Bayesian approach is that full inference is available regarding the population distributions. Prior information can conveniently be incorporated. Also, prior stochastic order is preserved to the posterior analysis. Apart from the two sample setting, the approach handles the matched pairs problem, the k-sample slippage problem, ordered ANOVA and ordered regression models. We illustrate by comparing two rather small samples, one of diabetic men, the other of diabetic women. Measurements are of androstenedione levels. Males are anticipated to produce levels which will tend to be higher than those of females. [ABSTRACT FROM AUTHOR]

In this article the most general class of bivariate distributions such that both conditional densities are Pearson Type VII, with fixed shape parameter, is fully characterized. Some of its properties and relations with other distributions are explored. The estimation of parameters is considered by the methods of maximum likelihood and pseudolikelihood and a method for random variate generation is presented along with a simulation experiment. Bivariate and multivariate extensions of the Pearson Type VII conditionals distribution are also discussed. [ABSTRACT FROM AUTHOR]

Blood coagulation, fibrinolytic and unspecific proteolytic parameters were investigated in 34 patients with acute myeloid leukemia. An increased activity of the coagulation system, documented by elevated thrombin-antithrombin III-complex (TAT) plasma levels, was found in 91% of the patients; 50% had increased elastase plasma levels. Hyperfibrinolysis, as shown by elevated fibrin split-product D-Dimer plasma levels, was detected in 91% of AML patients. Activation of these enzyme systems was not associated with relevant defects in blood coagulation or fibrinolysis in the majority of the patients investigated. In selected cases of promyelocytic M3 and monoblastic M5 leukemia, however, hypofibrinogenemia and α-plasmininhibitor defiency was found, most likely due to depletion of these proteins in the course of disseminated intravascular coagulation and secondary hyperfibrinolysis. Significant correlations were calculated between TAT and fibrinogen ( r = −0.57, P < 0.005), TAT and D-Dimer ( r = 0.89, P < 0.0005), and D-Dimer and α-plasmininhibitor ( r = −0.77, P < 0.0005) levels. Indications of a pathogenetic importance of primary hyperfibrinolysis or unspecific proteolysis for hypofibrinogenemia and α-PI deficiency were not found. [ABSTRACT FROM AUTHOR]

Copyright of Infection is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We describe an extremely rare case of trigeminal neuralgia caused by an arachnoid cyst located in the contralateral cerebellopontine angle. [ABSTRACT FROM AUTHOR]