Bayesian mixture model estimation of aerosol particle size distributions

In this paper, we examine approaches to estimate a Bayesian mixture model at both single and multiple time points for a sample of actual and simulated aerosol particle size distribution (PSD) data. For estimation of a mixture model at a single time point, we use Reversible Jump Markov Chain Monte Carlo (RJMCMC) to estimate mixture model parameters including the number of components which is assumed to be unknown. We compare the results of this approach to a commonly used estimation method in the aerosol physics literature. As PSD data is often measured over time, often at small time intervals, we also examine the use of an informative prior for estimation of the mixture parameters which takes into account the correlated nature of the parameters. The Bayesian mixture model offers a promising approach, providing advantages both in estimation and inference.

Model estimation of cerebral hemodynamics between blood flow and volume changes: A data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.

Benchmarks and the accuracy of GARCH model estimation

This paper reviews nine software packages with particular reference to their GARCH model estimation accuracy when judged against a respected benchmark. We consider the numerical consistency of GARCH and EGARCH estimation and forecasting. Our results have a number of implications for published research and future software development. Finally, we argue that the establishment of benchmarks for other standard non-linear models is long overdue.

Modified crop model estimation of depleted and potential soybean yield

Despite the great importance of soybeans in Brazil, there have been few applications of soybean crop modeling on Brazilian conditions. Thus, the objective of this study was to use modified crop models to estimate the depleted and potential soybean crop yield in Brazil. The climatic variable data used in the modified simulation of the soybean crop models were temperature, insolation and rainfall. The data set was taken from 33 counties (28 Sao Paulo state counties, and 5 counties from other states that neighbor São Paulo). Among the models, modifications in the estimation of the leaf area of the soybean crop, which includes corrections for the temperature, shading, senescence, CO2, and biomass partition were proposed; also, the methods of input for the model's simulation of the climatic variables were reconsidered. The depleted yields were estimated through a water balance, from which the depletion coefficient was estimated. It can be concluded that the adaptation soybean growth crop model might be used to predict the results of the depleted and potential yield of soybeans, and it can also be used to indicate better locations and periods of tillage.

Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.

NLCHECK: Stata module to check linearity assumption after model estimation

nlcheck is a simple diagnostic tool that can be used after fitting a model to quickly check the linearity assumption for a given predictor. nlcheck categorizes the predictor into bins, refits the model including dummy variables for the bins, and then performs a joint Wald test for the added parameters. Alternative, nlcheck uses linear splines for the adaptive model. Support for discrete variables is also provided. Optionally, nlcheck also displays a graph of the adjusted linear predictions from the original model and the adaptive model

The impact of the European sovereign debt crisis on the recovering from the financial crisis: Equity returns of the banking sector

This paper analyses, through a dynamic panel data model, the impact of the Financial and the European Debt crisis on the equity returns of the banking system. The model is also extended to specifically investigate the impact on countries who received rescue packages. The sample under analysis considers eleven countries from January 2006 to June 2013. The main conclusion is that there was in fact a structural change in banks’ excess returns due to the outbreak of the European Debt Crisis, when stock markets were still recovering from the Financial Crisis of 2008.

A simultaneous equations model of crash frequency by collision type for rural intersections

Safety at roadway intersections is of significant interest to transportation professionals due to the large number of intersections in transportation networks, the complexity of traffic movements at these locations that leads to large numbers of conflicts, and the wide variety of geometric and operational features that define them. A variety of collision types including head-on, sideswipe, rear-end, and angle crashes occur at intersections. While intersection crash totals may not reveal a site deficiency, over exposure of a specific crash type may reveal otherwise undetected deficiencies. Thus, there is a need to be able to model the expected frequency of crashes by collision type at intersections to enable the detection of problems and the implementation of effective design strategies and countermeasures. Statistically, it is important to consider modeling collision type frequencies simultaneously to account for the possibility of common unobserved factors affecting crash frequencies across crash types. In this paper, a simultaneous equations model of crash frequencies by collision type is developed and presented using crash data for rural intersections in Georgia. The model estimation results support the notion of the presence of significant common unobserved factors across crash types, although the impact of these factors on parameter estimates is found to be rather modest.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Virtual Visual Hulls: Example-Based 3D Shape Estimation from a Single Silhouette

Recovering a volumetric model of a person, car, or other object of interest from a single snapshot would be useful for many computer graphics applications. 3D model estimation in general is hard, and currently requires active sensors, multiple views, or integration over time. For a known object class, however, 3D shape can be successfully inferred from a single snapshot. We present a method for generating a ``virtual visual hull''-- an estimate of the 3D shape of an object from a known class, given a single silhouette observed from an unknown viewpoint. For a given class, a large database of multi-view silhouette examples from calibrated, though possibly varied, camera rigs are collected. To infer a novel single view input silhouette's virtual visual hull, we search for 3D shapes in the database which are most consistent with the observed contour. The input is matched to component single views of the multi-view training examples. A set of viewpoint-aligned virtual views are generated from the visual hulls corresponding to these examples. The 3D shape estimate for the input is then found by interpolating between the contours of these aligned views. When the underlying shape is ambiguous given a single view silhouette, we produce multiple visual hull hypotheses; if a sequence of input images is available, a dynamic programming approach is applied to find the maximum likelihood path through the feasible hypotheses over time. We show results of our algorithm on real and synthetic images of people.

Asset pricing in created markets

We investigate the applicability of the present-value asset pricing model to fishing quota markets by applying instrumental variable panel data estimation techniques to 15 years of market transactions from New Zealand's individual transferable quota (ITQ) market. In addition to the influence of current fishing rents, we explore the effect of market interest rates, risk, and expected changes in future rents on quota asset prices. The results indicate that quota asset prices are positively related to declines in interest rates, lower levels of risk, expected increases in future fish prices, and expected cost reductions from rationalization under the quota system. © 2007 American Agricultural Economics Association.

Trois essais en macroéconomie

Les fluctuations économiques représentent les mouvements de la croissance économique. Celle-ci peut connaître des phases d'accélération (expansion) ou de ralentissement (récession), voire même de dépression si la baisse de production est persistente. Les fluctuations économiques sont liées aux écarts entre croissance effective et croissance potentielle. Elles peuvent s'expliquer par des chocs d'offre et demande, ainsi que par le cycle du crédit. Dans le premier cas, les conditions de la production se trouvent modifiées. C'est le cas lorsque le prix des facteurs de production (salaires, prix des matières premières) ou que des facteurs externes influençant le prix des produits (taux de change) évolue. Ainsi, une hausse du prix des facteurs de production provoque un choc négatif et ralentit la croissance. Ce ralentissement peut être également dû à un choc de demande négatif provoqué par une hausse du prix des produits causée par une appréciation de la devise, engendrant une diminution des exportations. Le deuxième cas concerne les variables financières et les actifs financiers. Ainsi, en période d'expansion, les agents économiques s'endettent et ont des comportements spéculatifs en réaction à des chocs d'offre ou demande anticipés. La valeur des titres et actifs financiers augmente, provoquant une bulle qui finit par éclater et provoquer un effondrement de la valeur des biens. Dès lors, l'activité économique ne peut plus être financée. C'est ce qui génère une récession, parfois profonde, comme lors de la récente crise financière. Cette thèse inclut trois essais sur les fluctuations macroéconomiques et les cycles économiques, plus précisément sur les thèmes décrit ci-dessus. Le premier chapitre s'intéresse aux anticipations sur la politique monétaire et sur la réaction des agents écononomiques face à ces anticipations. Une emphase particulière est mise sur la consommation de biens durables et l'endettement relié à ce type de consommation. Le deuxième chapitre aborde la question de l'influence des variations du taux de change sur la demande de travail dans le secteur manufacturier canadien. Finalement, le troisième chapitre s'intéresse aux retombées économiques, parfois négatives, du marché immobilier sur la consommation des ménages et aux répercussions sur le prix des actifs immobiliers et sur l'endettement des ménages d'anticipations infondées sur la demande dans le marché immobilier. Le premier chapitre, intitulé ``Monetary Policy News Shocks and Durable Consumption'', fournit une étude sur le lien entre les dépenses en biens durables et les chocs monétaires anticipés. Nous proposons et mettons en oeuvre une nouvelle approche pour identifier les chocs anticipés (nouvelles) de politique monétaire, en les identifiant de manière récursive à partir des résidus d’une règle de Taylor estimée à l’aide de données de sondage multi-horizon. Nous utilisons ensuite les chocs anticipés inférer dans un modèle autorégressif vectoriel structurel (ARVS). L’anticipation d’une politique de resserrement monétaire mène à une augmentation de la production, de la consommation de biens non-durables et durables, ainsi qu’à une augmentation du prix réel des biens durables. Bien que les chocs anticipés expliquent une part significative des variations de la production et de la consommation, leur impact est moindre que celui des chocs non-anticipés sur les fluctuations économiques. Finalement, nous menons une analyse théorique avec un modèle d’équilibre général dynamique stochastique (EGDS) avec biens durables et rigidités nominales. Les résultats indiquent que le modèle avec les prix des biens durables rigides peut reproduire la corrélation positive entre les fonctions de réponse de la consommation de biens non-durables et durables à un choc anticipé de politique monétaire trouvées à l’aide du ARVS. Le second chapitre s'intitule ``Exchange Rate Fluctuations and Labour Market Adjustments in Canadian Manufacturing Industries''. Dans ce chapitre, nous évaluons la sensibilité de l'emploi et des heures travaillées dans les industries manufacturières canadiennes aux variations du taux de change. L’analyse est basée sur un modèle dynamique de demande de travail et utilise l’approche en deux étapes pour l'estimation des relations de cointégration en données de panel. Nos données sont prises d’un panel de 20 industries manufacturières, provenant de la base de données KLEMS de Statistique Canada, et couvrent une longue période qui inclut deux cycles complets d’appréciation-dépréciation de la valeur du dollar canadien. Les effets nets de l'appréciation du dollar canadien se sont avérés statistiquement et économiquement significatifs et négatifs pour l'emploi et les heures travaillées, et ses effets sont plus prononcés dans les industries davantage exposées au commerce international. Finalement, le dernier chapitre s'intitule ``Housing Market Dynamics and Macroprudential Policy'', dans lequel nous étudions la relation statistique suggérant un lien collatéral entre le marché immobilier and le reste de l'économique et si ce lien est davantage entraîné par des facteurs de demandes ou d'offres. Nous suivons également la littérature sur les chocs anticipés et examinons un cyle d'expansion-récession peut survenir de façon endogène la suite d'anticipations non-réalisées d'une hausse de la demande de logements. À cette fin, nous construisons un modèle néo-Keynésien au sein duquel le pouvoir d’emprunt du partie des consommateurs est limité par la valeur de leur patrimoine immobilier. Nous estimons le modèle en utilisant une méthode Bayésienne avec des données canadiennes. Nous évaluons la capacité du modèle à capter les caractéristiques principales de la consommation et du prix des maisons. Finalement, nous effectuons une analyse pour déterminer dans quelle mesure l'introduction d'un ratio prêt-à-la-valeur contracyclique peut réduire l'endettement des ménages et les fluctuations du prix des maisons comparativement à une règle de politique monétaire répondant à l'inflation du prix des maisons. Nous trouvons une relation statistique suggérant un important lien collatéral entre le marché immobilier et le reste de l'économie, et ce lien s'explique principalement par des facteurs de demande. Nous constatons également que l'introduction de chocs anticipés peut générer un cycle d'expansion-récession du marché immobilier, la récession faisant suite aux attentes non-réalisées par rapport à la demande de logements. Enfin, notre étude suggère également qu'un ratio contracyclique de prêt-à-la-valeur est une politique utile pour réduire les retombées du marché du logement sur la consommation par l'intermédiaire de la valeur garantie.

Real-time factor model forecasting and the effects of instability

Factor forecasting models are shown to deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one quarter ahead) horizon. Excluding the pre-Great Moderation years from the factor forecasting model estimation period (but not from the data used to extract factors) results in a marked fillip in factor model forecast accuracy, but does the same for the AR model forecasts. The relative performance of the factor models compared to the AR models is largely unaffected by whether the exercise is in real time or is pseudo out-of-sample.