Search results for "PREDICTION"
showing 10 items of 511 documents
Fitting generalized linear models with unspecified link function: A P-spline approach
2008
Generalized linear models (GLMs) outline a wide class of regression models where the effect of the explanatory variables on the mean of the response variable is modelled throughout the link function. The choice of the link function is typically overlooked in applications and the canonical link is commonly used. The estimation of GLMs with unspecified link function is discussed, where the linearity assumption between the link and the linear predictor is relaxed and the unspecified relationship is modelled flexibly by means of P-splines. An estimating algorithm is presented, alternating estimation of two working GLMs up to convergence. The method is applied to the analysis of quit behavior of…
A note on adjusted responses, fitted values and residuals in Generalized Linear Models
2014
Adjusted responses, adjusted fitted values and adjusted residuals are known to play in Generalized Linear Models the role played in Linear Models by observations, fitted values and ordinary residuals. We think this parallelism, which was widely recognized and used in the early literature on Generalized Linear Models, has been somewhat overlooked in more recent presentations. We revise this parallelism, systematizing and proving some results that are either scattered or not satisfactorily spelled out in the literature. In particular, we formally derive the asymptotic dispersion matrix of the (scaled) adjusted residuals, by proving that in Generalized Linear Models the fitted values are asym…
Analysis and modelling of wind speed in New York
2010
In this paper we propose an ARMA time-series model for the wind speed at a single spatial location, and estimate it on in-sample data recorded in three different wind farm regions in New York state. The data have a three-hour granularity, but based on applications to financial wind derivatives contracts, we also consider daily average wind speeds. We demonstrate that there are large discrepancies in the behaviour of daily average and three-hourly wind speed records. The validation procedure based on out-of-sample observations reflects that the proposed model is reliable and can be used for various practical applications, like, for instance, weather prediction, pricing of financial wind cont…
Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data
2015
The paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate-specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian infe…
Varying-time random effects models for longitudinal data: unmixing and temporal interpolation of remote-sensing data
2008
Remote sensing is a helpful tool for crop monitoring or vegetation-growth estimation at a country or regional scale. However, satellite images generally have to cope with a compromise between the time frequency of observations and their resolution (i.e. pixel size). When concerned with high temporal resolution, we have to work with information on the basis of kilometric pixels, named mixed pixels, that represent aggregated responses of multiple land cover. Disaggreggation or unmixing is then necessary to downscale from the square kilometer to the local dynamic of each theme (crop, wood, meadows, etc.). Assuming the land use is known, that is to say the proportion of each theme within each m…
Global stability of protein folding from an empirical free energy function
2013
The principles governing protein folding stand as one of the biggest challenges of Biophysics. Modeling the global stability of proteins and predicting their tertiary structure are hard tasks, due in part to the variety and large number of forces involved and the difficulties to describe them with sufficient accuracy. We have developed a fast, physics-based empirical potential, intended to be used in global structure prediction methods. This model considers four main contributions: Two entropic factors, the hydrophobic effect and configurational entropy, and two terms resulting from a decomposition of close-packing interactions, namely the balance of the dispersive interactions of folded an…
A Bayesian SIRS model for the analysis of respiratory syncytial virus in the region of Valencia, Spain
2014
We present a Bayesian stochastic susceptible-infected-recovered-susceptible (SIRS) model in discrete time to understand respiratory syncytial virus dynamics in the region of Valencia, Spain. A SIRS model based on ordinary differential equations has also been proposed to describe RSV dynamics in the region of Valencia. However, this continuous-time deterministic model is not suitable when the initial number of infected individuals is small. Stochastic epidemic models based on a probability of disease transmission provide a more natural description of the spread of infectious diseases. In addition, by allowing the transmission rate to vary stochastically over time, the proposed model provides…
A convolutional neural network for virtual screening of molecular fingerprints
2019
In the last few years, Deep Learning (DL) gained more and more impact on drug design because it allows a huge increase of the prediction accuracy in many stages of such a complex process. In this paper a Virtual Screening (VS) procedure based on Convolutional Neural Networks (CNN) is presented, that is aimed at classifying a set of candidate compounds as regards their biological activity on a particular target protein. The model has been trained on a dataset of active/inactive compounds with respect to the Cyclin-Dependent Kinase 1 (CDK1) a very important protein family, which is heavily involved in regulating the cell cycle. One qualifying point of the proposed approach is the use of molec…
Mechanistic Understanding of Food Effects: Water Diffusivity in Gastrointestinal Tract Is an Important Parameter for the Prediction of Disintegration…
2013
Much interest has been expressed in this work on the role of water diffusivity in the release media as a new parameter for predicting drug release. NMR was used to measure water diffusivity in different media varying in their osmolality and viscosity. Water self-diffusion coefficients in sucrose, sodium chloride, and polymeric hydroxypropyl methylcellulose (HPMC) solutions were correlated with water uptake, disintegration, and drug release rates from trospium chloride immediate release tablets. The water diffusivity in sucrose solutions was significantly reduced compared to polymeric HPMC and molecular sodium chloride solutions. Water diffusivity was found to be a function of sucrose concen…
Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
2022
Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction …