Belén García-Mora

Education and the Determinants of Job Satisfaction

Abstract Using a representative sample of Spanish individuals, we explore the effects of workers’ education on self‐assessed satisfaction with diverse specific aspects of their jobs. We find that the effects of education level on job satisfaction differ, both in size and direction, according to the aspect of the job considered, especially after controlling for actual job attributes and other workers’ characteristics. We also find that workers’ perceptions of the match between education and employment are relevant as determinants of job satisfaction irrespective of workers’ education level.

The influence of geographical concentration and structural characteristics on the survival chance of textile firms

Purpose – The purpose of this paper is to evaluate the contribution of location (geographical concentration) and firm structure (age and subsector) to the risk of business failure.Design/methodology/approach – The Markovs regression analysis was made for a sample of over 11,700 Spanish textile‐clothing firms.Findings – The results obtained from the analysis suggest that the risk of business failure is increased by some risk factors relative to the structural characteristics of the firms (younger firms and specialization in low‐tech activities), and under determined locational circumstances.Research limitations/implications – Our conclusions have been obtained starting from a sample of manufacturing firms in the Spanish textile sector. An extension of this work would be to test its robustness in other countries (for example, Italy or Portugal) and/or for other industries such as footwear and furniture.Practical implications – Explaining the different levels of risk business failure shown by firms in an in...

Modeling the recurrence-progression process in bladder carcinoma

Wei-Lin-Weissfeld (WLW) method is used to analyze different states of the superficial vesical carcinoma distinguishing between recurrences and the possibility of progression. Two approaches are considered in this analysis to represent different aspects of the disease from a clinical point of view: the first one attempts to focus on the effect of the clinico-pathological factors on recurrences by regarding a progression before the recurrence as a censoring event, meanwhile the second one analyzes these same effects on either recurrence or progression, whichever comes first. A predictive model of recurrence or progression based on clinico-pathological factors is presented.

Modeling bladder cancer using a Markov process with multiple absorbing states

The aim of this study is to analyze survival until extirpation in bladder carcinoma. For this it is necessary to study the different stages of this chronic disease: multiple recurrences and progression. The sum of two random independent variables is considered, each of them being associated with a homogeneous time-continuous Markovian process of multiple absorbing states. The distribution function of the variable sum is modeled by means of a new associated Markov process. In order to maintain manageable problem dimension we have applied the Frechet derivate and the Kronecker matrix representation.

A flowgraph model for bladder carcinoma

BackgroundSuperficial bladder cancer has been the subject of numerous studies for many years, but the evolution of the disease still remains not well understood. After the tumor has been surgically removed, it may reappear at a similar level of malignancy or progress to a higher level. The process may be reasonably modeled by means of a Markov process. However, in order to more completely model the evolution of the disease, this approach is insufficient. The semi-Markov framework allows a more realistic approach, but calculations become frequently intractable. In this context, flowgraph models provide an efficient approach to successfully manage the evolution of superficial bladder carcinoma. Our aim is to test this methodology in this particular case.ResultsWe have built a successful model for a simple but representative case.ConclusionThe flowgraph approach is suitable for modeling of superficial bladder cancer.

Computing survival functions of the sum of two independent Markov processes: an application to bladder carcinoma treatment

The study of the sum of two independent phase-type (PH)-distributed variables, each of them being associated with a Markovian process with one absorbing state, is considered in this paper. The distribution function of the variable sum is computed, obtaining a new PH-distributed function of higher order. As the order increases in the new function, the exponential function of a block upper triangular matrix is calculated in terms of its respective blocks to reduce the dimension of the problem. The obtained results are applied to bladder carcinoma data.

Bayesian prediction for flowgraph models with covariates. An application to bladder carcinoma

Statistical Flowgraph Models are an efficient tool to model multi-state stochastic processes. They support both frequentist and Bayesian approaches. Inclusion of covariates is also available. In this paper we propose an easy way to perform a Bayesian approach with covariates. Results are presented with an application to bladder carcinoma data.

Modelling the failure risk for water supply networks with interval-censored data

Abstract In reliability, sometimes some failures are not observed at the exact moment of the occurrence. In that case it can be more convenient to approximate them by a time interval. In this study, we have used a generalized non-linear model developed for interval-censored data to treat the life time of a pipe from its time of installation until its failure. The aim of this analysis was to identify those network characteristics that may affect the risk of failure and we make an exhaustive validation of this analysis. The results indicated that certain characteristics of the network negatively affected the risk of failure of the pipe: an increase in the length and pressure of the pipes, a small diameter, some materials used in the manufacture of pipes and the traffic on the street where the pipes are located. Once the model has been correctly fitted to our data, we also provided simple tables that will allow companies to easily calculate the pipe׳s probability of failure in a future.

A Markov model for analyzing the evolution of bladder carcinoma

A Markovian approach to analyze different states of the superficial vesical carcinoma is considered, taking into account up to two recurrences and the possibility of progression. So, three transient states are considered: free of disease, first, and second recurrence; and an absorbent state, the progression. A methodology based in phase-type distributions is also used, that allows the usual quantities of interest in survival studies to be expressed in a well-structured form. This type of distribution has shown its utility in queue theory, and has the advantage that mathematical expressions can be presented in a closed form that allows algebraic treatment.

Modeling dependence in the inter–failure times. An analysis in Reliability models by Markovian Arrival Processes

Abstract The most common assumptions in reliability studies are that failures occur independently and with the same distribution. However, these two assumptions are unrealistic in practice since inter-failure times are usually correlated and not identically distributed. In this sense the Markov Arrival Process (MAP) is an active research field for managing these features. We study two versions of the MAP approach. The first one is a model which we developed in previous works. The second one is based on a non-stationary M A P of second order. We compare the results of both models with simulated data.