Search results for "probability"

Study Design in Causal Models

2014

The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing-data mechanism together with the causal structure and allow the direct application of causal calculus in the estimation of the causal effects. The flow of the study is visualized by ordering the nodes of the causal diagram in two dimensions by their causal order and the time of the observation. Conclusions on whether a causal or observational relationship can be estimated from the coll…

Characterization of the parameters of interior permanent magnet synchronous motors for a loss model algorithm

2017

Abstract The paper provides the results of a detailed experimental study on the variations of the characteristics of an interior permanent magnet synchronous motor, when load, speed and/or magnetization conditions vary. In particular, the characterization is carried out by assessing, for several working conditions, the motor parameters that influence its efficiency. From the knowledge of the variability of these parameters, it is possible to develop a dynamic model of the motor, which accurately describes its behaviour and allows estimating the power losses for whatever speed and load. In order to validate the model, the values of the power losses obtained by using the model are compared wi…

Special issue on ambient advancements in intelligent computational sciences

2018

R Graphics (3rd Edition)

2020

Barriers to women entrepreneurship. Different methods, different results?

2016

Building on research by Akehurst et al. (Serv Ind J 32:2489-2505, 2012), this study analysed internal and external factors in women entrepreneurship and linked these factors to the barriers that women face when starting businesses. To do so, two contrasting statistical techniques were used: PLS and QCA. After analysing results from each of these techniques, we observed that family duties and difficulties in obtaining financing (both internal and external) were the main factors related to barriers faced by women entrepreneurs.

How to empower women’s entrepreneurship? An analysis of women’s sport employment and contextual variables in European Union countries using a fuzzy a…

2021

Although the number of women entrepreneurs has increased in recent years, it is still lower than that of men. In addition, although the sports sector has been characterized by its growth in recent years and contributes to the GDP of the countries by generating employment, the role that this has within female entrepreneurship has never been analysed. Therefore, the objective of this study is to know the combinations of conditions (female employment in sports, government support, financing for entrepreneurs, perception of entrepreneurial opportunities and capacities, glass ceiling index and masculine values in society) that generate high levels of female entrepreneurship in the countries of t…

Bayesian joint modeling for assessing the progression of chronic kidney disease in children.

2016

Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is modeled in terms of a survival model with competing risks and left truncation. The model is applied to data coming from ReVaPIR, a project involving children with chronic kidney disease whose evolution is mainly assessed through longitudinal measurements of glomerular filtration rate.

Bayesian hierarchical Poisson models with a hidden Markov structure for the detection of influenza epidemic outbreaks

2015

Considerable effort has been devoted to the development of statistical algorithms for the automated monitoring of influenza surveillance data. In this article, we introduce a framework of models for the early detection of the onset of an influenza epidemic which is applicable to different kinds of surveillance data. In particular, the process of the observed cases is modelled via a Bayesian Hierarchical Poisson model in which the intensity parameter is a function of the incidence rate. The key point is to consider this incidence rate as a normal distribution in which both parameters (mean and variance) are modelled differently, depending on whether the system is in an epidemic or non-epide…

Bayesian Markov switching models for the early detection of influenza epidemics

2008

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In this paper, a Markov switching model is introduced to determine the epidemic and non-epidemic periods from influenza surveillance data: the process of differenced incidence rates is modelled either with a first-order autoregressive process or with a Gaussian white-noise process depending on whether the system is in an epidemic or in a non-epidemic phase. The transition between phases of the disease is modelled as a Markovian process. Bayesian inference is carried out on the former model to detect influenza epidemics at the very moment of their onset. Moreover, t…

Bayesian joint ordinal and survival modeling for breast cancer risk assessment

2016

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model. Time-to-event is modeled through a left-truncated proportionalhazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects. General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the …