Search results for "Additive model"
showing 8 items of 28 documents
Do Saharan Dust Days Carry a Risk of Hospitalization From Respiratory Diseases for Citizens of the Canary Islands (Spain)?
2021
Background: Saharan dust meets the Canary Islands at the beginning of its westward path across the North Atlantic, exceeding the European daily levels for PM10; for this reason, their two provincial capital cities, constitute optimal sites where to evaluate the health effects of this natural event. Objectives: To assess the short-term association between Saharan Dust Days (SDDs) and respiratory morbidity in the two capital cities. Methods: We carried out a time-series analysis with daily emergency hospital admissions due to all respiratory system diseases, chronic obstructive pulmonary disease (COPD) and asthma between 2001 and 2005, assessing the independent effect of SDDs, defined accordi…
Quantifying unpredictability: A multiple-model approach based on satellite imagery data from Mediterranean ponds.
2017
Fluctuations in environmental parameters are increasingly being recognized as essential features of any habitat. The quantification of whether environmental fluctuations are prevalently predictable or unpredictable is remarkably relevant to understanding the evolutionary responses of organisms. However, when characterizing the relevant features of natural habitats, ecologists typically face two problems: (1) gathering long-term data and (2) handling the hard-won data. This paper takes advantage of the free access to long-term recordings of remote sensing data (27 years, Landsat TM/ETM+) to assess a set of environmental models for estimating environmental predictability. The case study inclu…
Forecasting time series with missing data using Holt's model
2009
This paper deals with the prediction of time series with missing data using an alternative formulation for Holt's model with additive errors. This formulation simplifies both the calculus of maximum likelihood estimators of all the unknowns in the model and the calculus of point forecasts. In the presence of missing data, the EM algorithm is used to obtain maximum likelihood estimates and point forecasts. Based on this application we propose a leave-one-out algorithm for the data transformation selection problem which allows us to analyse Holt's model with multiplicative errors. Some numerical results show the performance of these procedures for obtaining robust forecasts.
Optimal designs for a one-way layout with covariates
2000
Abstract For the general class of Φ q -criteria optimal designs are characterized which reflect the inherent symmetry in a one-way layout with covariates. In particular, the eigenvalues of the covariance matrices are related to those in suitably chosen marginal models depending on the underlying interaction structure.
Efficiency Bounds for Product Designs in Linear Models
1999
We provide lower efficiency bounds for the best product design for an additive multifactor linear model. The A-optimality criterion is used to demonstrate that out bounds are better than the conventional bounds. Applications to other criteria, such as IMSE (integrated mean squared error) criterion are also indicated. In all the cases, the best product design appears to perform better when there are more levels in each factor but decreases when more factors are included. Explicit efficiency formulas for non-additive models are also constructed.
A comparison of nonparametric methods in the graduation of mortality: Application to data from the Valencia Region (Spain)
2006
[EN] The nonparametric graduation of mortality data aims to estimate death rates by carrying out a smoothing of the crude rates obtained directly from original data. The main difference with regard to parametric models is that the assumption of an age-dependent function is unnecessary, which is advantageous when the information behind the model is unknown, as one cause of error is often the choice of an inappropriate model. This paper reviews the various alternatives and presents their application to mortality data from the Valencia Region, Spain. The comparison leads us to the conclusion that the best model is a smoothing by means of Generalised Additive Models (GAM) with splines. The most…
GAMLSS for high-variability data: an application to liver fibrosis case
2020
In this paper, we propose management of the problem caused by overdispersed data by applying the generalized additive model for location, scale and shape framework (GAMLSS) as introduced by Rigby and Stasinopoulos (2005). The idea of using a GAMLSS approach for handling our problem comes from the idea of Aitkin (1996) consisting in the use of an EM maximum likelihood estimation algorithm (Dempster, Laird, and Rubin, 1977) to deal with overdispersed generalized linear models (GLM). As in the GLM case, the algorithm is initially derived as a form of Gaussian quadrature assuming a normal mixing distribution. The GAMLSS specification allows the extension of the Aitkin algorithm to probability d…
Bending Work Time: Curvilinear Relationship Between Working Time Dimensions and Psychological and Somatic Symptoms.
2020
Objectives Study examines the curvilinear associations of working time dimensions (working hours, time pressure, work schedules, and control of work time and pace) on psychological and somatic symptoms. Methods Representative Finnish Quality-of-Work-Life Surveys conducted in 2003, 2008 and 2013 were restricted to those (N=11,165) regularly working over 10h/week with more than one-year tenure in their job. Generalised additive models were utilised in analysis. Results Working hours had U-shaped relationships with psychosomatic symptoms, while time pressure had a threshold effect. Work pace control had linear effect. The effects of work time control and work schedules were insignificant. Ther…