304,590 research outputs found

    Penalized likelihood estimation and iterative kalman smoothing for non-gaussian dynamic regression models

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    Dynamic regression or state space models provide a flexible framework for analyzing non-Gaussian time series and longitudinal data, covering for example models for discrete longitudinal observations. As for non-Gaussian random coefficient models, a direct Bayesian approach leads to numerical integration problems, often intractable for more complicated data sets. Recent Markov chain Monte Carlo methods avoid this by repeated sampling from approximative posterior distributions, but there are still open questions about sampling schemes and convergence. In this article we consider simpler methods of inference based on posterior modes or, equivalently, maximum penalized likelihood estimation. From the latter point of view, the approach can also be interpreted as a nonparametric method for smoothing time-varying coefficients. Efficient smoothing algorithms are obtained by iteration of common linear Kalman filtering and smoothing, in the same way as estimation in generalized linear models with fixed effects can be performed by iteratively weighted least squares estimation. The algorithm can be combined with an EM-type method or cross-validation to estimate unknown hyper- or smoothing parameters. The approach is illustrated by applications to a binary time series and a multicategorical longitudinal data set

    Building energy performance characterisation based on dynamic analysis and co-heating test

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    A demonstration zero-carbon neighborhood is being raised in the city of Kortrijk, Belgium in the framework of the ECO-Life project within the CONCERTO initiative. A holistic approach is applied to achieve the zero-carbon targets, considering all aspects that are relevant for energy supply. Accordingly, alongside the integration of renewable energy sources in the community, a low-temperature district heating system is being implemented to cover the heat demand. In this context, full scale testing of building thermal performances, by use of a co-heating test and flux measurements, can be useful to analyze the thermal performance of the building envelope in situ. For that reason, as part of a more general study regarding low-energy building, co-heating test, blower-door test and flux measurements in several apartments were executed. Therefore, the paper focuses on characterization of the thermal dynamic behavior of an apartment, as a first approximation of data analysis of a monitoring system involving whole buildings. In addition, in the present study, the capability of linear regression techniques to characterize the thermal behavior of a newly built low-energy apartment in Belgium is investigated. The strengths and weaknesses of different models are identified. The limitation and possibilities of regression models are evaluated in the face of their applicability as a simplified building equation model. The identified model structure is going to be used within a complex simulation model of an entire district heating system with around 200 dwelling. Finally, the potential of this kind of regression models to be used as part of the operational control scheme of a district heating system is presented

    Computationally Efficient Data-Driven MPC for Agile Quadrotor Flight

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    This paper develops computationally efficient data-driven model predictive control (MPC) for Agile quadrotor flight. Agile quadrotors in high-speed flights can experience high levels of aerodynamic effects. Modeling these turbulent aerodynamic effects is a cumbersome task and the resulting model may be overly complex and computationally infeasible. Combining Gaussian Process (GP) regression models with a simple dynamic model of the system has demonstrated significant improvements in control performance. However, direct integration of the GP models to the MPC pipeline poses a significant computational burden to the optimization process. Therefore, we present an approach to separate the GP models to the MPC pipeline by computing the model corrections using reference trajectory and the current state measurements prior to the online MPC optimization. This method has been validated in the Gazebo simulation environment and has demonstrated of up to 50%50\% reduction in trajectory tracking error, matching the performance of the direct GP integration method with improved computational efficiency.Comment: 6 pages, accepted in ACC 2023 (American Control Conference, 2023

    Quasi-Monte Carlo for Highly Structured Generalised Response Models

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    Highly structured generalised response models, such as generalised linear mixed models and generalised linear models for time series regression, have become an indispensable vehicle for data analysis and inference in many areas of application. However, their use in practice is hindered by high-dimensional intractable integrals. Quasi-Monte Carlo (QMC) is a dynamic research area in the general problem of high-dimensional numerical integration, although its potential for statistical applications is yet to be fully explored. We survey recent research in QMC, particularly lattice rules, and report on its application to highly structured generalised response models. New challenges for QMC are identified and new methodologies are developed. QMC methods are seen to provide significant improvements compared with ordinary Monte Carlo methods

    Towards Developing a Travel Time Forecasting Model for Location-Based Services: a Review

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    Travel time forecasting models have been studied intensively as a subject of Intelligent Transportation Systems (ITS), particularly in the topics of advanced traffic management systems (ATMS), advanced traveler information systems (ATIS), and commercial vehicle operations (CVO). While the concept of travel time forecasting is relatively simple, it involves a notably complicated task of implementing even a simple model. Thus, existing forecasting models are diverse in their original formulations, including mathematical optimizations, computer simulations, statistics, and artificial intelligence. A comprehensive literature review, therefore, would assist in formulating a more reliable travel time forecasting model. On the other hand, geographic information systems (GIS) technologies primarily provide the capability of spatial and network database management, as well as technology management. Thus, GIS could support travel time forecasting in various ways by providing useful functions to both the managers in transportation management and information centers (TMICs) and the external users. Thus, in developing a travel time forecasting model, GIS could play important roles in the management of real-time and historical traffic data, the integration of multiple subsystems, and the assistance of information management. The purpose of this paper is to review various models and technologies that have been used for developing a travel time forecasting model with geographic information systems (GIS) technologies. Reviewed forecasting models in this paper include historical profile approaches, time series models, nonparametric regression models, traffic simulations, dynamic traffic assignment models, and neural networks. The potential roles and functions of GIS in travel time forecasting are also discussed.

    Process Design and Optimization (MLS-S03): Model Identification by Gradient Methods

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    This lecture describes the following topics: • Dynamic Models     - Conservation of Mass (Concentration Measurements)     - Conservation of Energy (Calorimetry)     - Beer’s Law (Spectroscopy) • Integration of Dynamic Models     - Euler’s Method     - Runge-Kutta’s Methods (RK) • Linear Regression Problems (OLS)     - Calibration-free Calorimetry and Spectroscopy • Gradient-based Nonlinear Regression Methods (NLR)     - Steepest Descent Method     - Newton-Raphson and Newton-Gauss Methods (NG)     - Newton-Gauss Levenberg Marquardt Method (NGLM) • Reference

    Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models

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    We develop numerically stable and accurate stochastic simulation approaches for solving dynamic economic models. First, instead of standard least-squares methods, we examine a variety of alternatives, including least-squares methods using singular value decomposition and Tikhonov regularization, least-absolute deviations methods, and principal component regression method, all of which are numerically stable and can handle ill-conditioned problems. Second, instead of conventional Monte Carlo integration, we use accurate quadrature and monomial integration. We test our generalized stochastic simulation algorithm (GSSA) in three applications: the standard representative agent neoclassical growth model, a model with rare disasters and a multi-country models with hundreds of state variables. GSSA is simple to program, and MATLAB codes are provided.Stochastic simulation; generalized stochastic simulation algorithm (GSSA), parameterized expectations algorithm (PEA); least absolute deviations (LAD); linear programming; regularization.

    Integrating Dynamic Subsurface Habitat Metrics Into Species Distribution Models

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    Species distribution models (SDMs) have become key tools for describing and predicting species habitats. In the marine domain, environmental data used in modeling species distributions are often remotely sensed, and as such have limited capacity for interpreting the vertical structure of the water column, or are sampled in situ, offering minimal spatial and temporal coverage. Advances in ocean models have improved our capacity to explore subsurface ocean features, yet there has been limited integration of such features in SDMs. Using output from a data-assimilative configuration of the Regional Ocean Modeling System, we examine the effect of including dynamic subsurface variables in SDMs to describe the habitats of four pelagic predators in the California Current System (swordfish Xiphias gladius, blue sharks Prionace glauca, common thresher sharks Alopias vulpinus, and shortfin mako sharks lsurus oxyrinchus). Species data were obtained from the California Drift Gillnet observer program (1997-2017). We used boosted regression trees to explore the incremental improvement enabled by dynamic subsurface variables that quantify the structure and stability of the water column: isothermal layer depth and bulk buoyancy frequency. The inclusion of these dynamic subsurface variables significantly improved model explanatory power for most species. Model predictive performance also significantly improved, but only for species that had strong affiliations with dynamic variables (swordfish and shortfin mako sharks) rather than static variables (blue sharks and common thresher sharks). Geospatial predictions for all species showed the integration of isothermal layer depth and bulk buoyancy frequency contributed value at the mesoscale level (\u3c 100 km) and varied spatially throughout the study domain. These results highlight the utility of including dynamic subsurface variables in SDM development and support the continuing ecological use of biophysical output from ocean circulation models

    Predicting time to graduation at a large enrollment American university

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    The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto's Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).Comment: 28 pages, 11 figure
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