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Modelos de regressão quantílica; Quantile Regression Models

Santos, Bruno Ramos dos
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 02/03/2012 Português
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66.66%
Este trabalho trata de modelos de regressão quantílica. Foi feita uma introdução a essa classe de modelos para motivar a discussão. Em seguida, conceitos inferenciais, como estimação, intervalos de confiança, testes de hipóteses para os parâmetros são discutidos, acompanhados de alguns estudos de simulação. Para analisar a qualidade do ajuste, são apresentados o coeficiente de determinação e um teste de falta de ajuste para modelos de regressão quantílica. Também é proposta a utilização de gráficos para análise da qualidade do ajuste considerando a distribuição Laplace Assimétrica. Uma aplicação utilizando um banco de dados com informação sobre renda no Brasil foi utilizado para exemplificar os tópicos discutidos durante o texto.; This work is about quantile regression models. An introduction was made to this class of models to motivate the discussion. Then, inferential concepts, like estimation, confidence intervals, tests of hypothesis for the parameters are discussed, followed by some simulation studies. To analyse goodness of fit, a coefficient of determination and a lack-of-fit test for quantile regression models are presented. Its also proposed the use of graphs for the goodness of fit analysis considering the Asymmetric Laplace Distribution. An application using a data base with information about income in Brazil was used to exemplify the topics discussed during the text.

Quantile regression for mixed-effects models = : Regressão quantílica para modelos de efeitos mistos; Regressão quantílica para modelos de efeitos mistos

Christian Eduardo Galarza Morales
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 16/03/2015 Português
Relevância na Pesquisa
56.49%
Os dados longitudinais são frequentemente analisados usando modelos de efeitos mistos normais. Além disso, os métodos de estimação tradicionais baseiam-se em regressão na média da distribuição considerada, o que leva a estimação de parâmetros não robusta quando a distribuição do erro não é normal. Em comparação com a abordagem de regressão na média convencional, a regressão quantílica (RQ) pode caracterizar toda a distribuição condicional da variável de resposta e é mais robusta na presença de outliers e especificações erradas da distribuição do erro. Esta tese desenvolve uma abordagem baseada em verossimilhança para analisar modelos de RQ para dados longitudinais contínuos correlacionados através da distribuição Laplace assimétrica (DLA). Explorando a conveniente representação hierárquica da DLA, a nossa abordagem clássica segue a aproximação estocástica do algoritmo EM (SAEM) para derivar estimativas de máxima verossimilhança (MV) exatas dos efeitos fixos e componentes de variância em modelos lineares e não lineares de efeitos mistos. Nós avaliamos o desempenho do algoritmo em amostras finitas e as propriedades assintóticas das estimativas de MV através de experimentos empíricos e aplicações para quatro conjuntos de dados reais. Os algoritmos SAEMs propostos são implementados nos pacotes do R qrLMM() e qrNLMM() respectivamente.; Longitudinal data are frequently analyzed using normal mixed effects models. Moreover...

Energy prices and CO2 emission allowance prices : a quantile regression approach

Hammoudeh, Shawkat; Nguyen, Duc Khuong; Sousa, Ricardo M.
Fonte: Universidade do Minho. Núcleo de Investigação em Políticas Económicas (NIPE) Publicador: Universidade do Minho. Núcleo de Investigação em Políticas Económicas (NIPE)
Tipo: Trabalho em Andamento
Publicado em //2014 Português
Relevância na Pesquisa
66.54%
We use a quantile regression framework to investigate the impact of changes in crude oil prices, natural gas prices, coal prices, and electricity prices on the distribution of the CO2 emission allowance prices in the United States. We find that: (i) an increase in the crude oil price generates a substantial drop in the carbon prices when the latter is very high; (ii) changes in the natural gas prices have a negative effect on the carbon prices when they are very low but have a positive effect when they are quite high; (iii) the impact of the changes in the electricity prices on the carbon prices can be positive in the right tail of the distribution; and (iv) the coal prices exert a negative effect on the carbon prices.; COMPETE, QREN, FEDER, Fundação para a Ciência e a Tecnologia (FCT)

Modeling Portuguese water demand with quantile regression

Cardoso, Maria Leonor Bandeira de Melo Barreiros
Fonte: Instituto Universitário de Lisboa Publicador: Instituto Universitário de Lisboa
Tipo: Dissertação de Mestrado
Publicado em //2013 Português
Relevância na Pesquisa
66.54%
Project submitted as partial requirement for the conferral of Master in Economics / Classificação JEL: C31, Q25; A literatura recente sobre a estimação da procura de água indica que a elasticidade-preço da procura de água residencial pode não ser constante ao longo da distribuição do consumo de água. Se assim for, a elasticidade-preço da média da amostra ou da população de consumidores em questão é insuficiente para prever os possíveis impactos de uma mudança de preço. A aplicação da regressão de quantis para estimar os impactos esperados das variáveis explicativas normalmente aceites na literatura, como é o caso do preço (marginal ou médio), o rendimento ou variáveis relacionadas com o tempo, pretende mostrar que tais impactos são diferentes dependendo dos níveis de consumo de água. Em diferentes percentis da distribuição o efeito dos regressores é diferente. Os resultados de uma amostra de 383 famílias Portuguesas que são sujeitas a tarifas crescentes por bloco mostram precisamente isso. Especialmente, quando se consideram os efeitos da variável preço, em que as famílias com baixos níveis de consumo de água reagem mais às variações de preço em comparação com as famílias com consumos superiores. Este resultado põe em causa um objetivo comum da estrutura de preços aplicada...

Spatiotemporal quantile regression for detecting distributional changes in environmental processes

Reich, Brian J
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /08/2012 Português
Relevância na Pesquisa
46.91%
Climate change may lead to changes in several aspects of the distribution of climate variables, including changes in the mean, increased variability, and severity of extreme events. In this paper, we propose using spatiotemporal quantile regression as a flexible and interpretable method for simultaneously detecting changes in several features of the distribution of climate variables. The spatiotemporal quantile regression model assumes that each quantile level changes linearly in time, permitting straight-forward inference on the time trend for each quantile level. Unlike classical quantile regression which uses model-free methods to analyze a single quantile or several quantiles separately, we take a model-based approach which jointly models all quantiles, and thus the entire response distribution. In the spatiotemporal quantile regression model, each spatial location has its own quantile function that evolves over time, and the quantile functions are smoothed spatially using Gaussian process priors. We propose a basis expansion for the quantile function that permits a closed-form for the likelihood, and allows for residual correlation modeling via a Gaussian spatial copula. We illustrate the methods using temperature data for the southeast US from the years 1931–2009. For these data...

Censored quantile regression with recursive partitioning-based weights

Wey, Andrew; Wang, Lan; Rudser, Kyle
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.83%
Censored quantile regression provides a useful alternative to the Cox proportional hazards model for analyzing survival data. It directly models the conditional quantile of the survival time and hence is easy to interpret. Moreover, it relaxes the proportionality constraint on the hazard function associated with the popular Cox model and is natural for modeling heterogeneity of the data. Recently, Wang and Wang (2009. Locally weighted censored quantile regression. Journal of the American Statistical Association 103, 1117–1128) proposed a locally weighted censored quantile regression approach that allows for covariate-dependent censoring and is less restrictive than other censored quantile regression methods. However, their kernel smoothing-based weighting scheme requires all covariates to be continuous and encounters practical difficulty with even a moderate number of covariates. We propose a new weighting approach that uses recursive partitioning, e.g. survival trees, that offers greater flexibility in handling covariate-dependent censoring in moderately high dimensions and can incorporate both continuous and discrete covariates. We prove that this new weighting scheme leads to consistent estimation of the quantile regression coefficients and demonstrate its effectiveness via Monte Carlo simulations. We also illustrate the new method using a widely recognized data set from a clinical trial on primary biliary cirrhosis.

SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES

Zhu, Liping; Huang, Mian; Li, Runze
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /10/2012 Português
Relevância na Pesquisa
46.83%
This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mild conditions, we show that the simple linear quantile regression offers a consistent estimate of the index parameter vector. This is a surprising and interesting result because the single-index model is possibly misspecified under the linear quantile regression. With a root-n consistent estimate of the index vector, one may employ a local polynomial regression technique to estimate the conditional quantile function. This procedure is computationally efficient, which is very appealing in high-dimensional data analysis. We show that the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of the index vector were known. The methodologies are demonstrated through comprehensive simulation studies and an application to a real dataset.

Household characteristics and calorie intake in rural India: a quantile regression approach

Sinha, Kompal
Fonte: Universidade Nacional da Austrália Publicador: Universidade Nacional da Austrália
Tipo: Working/Technical Paper Formato: 531407 bytes; 354 bytes; application/pdf; application/octet-stream
Português
Relevância na Pesquisa
56.59%
The present paper investigates the nutrition demand pattern for rural households in India. The non-parametric approach of quantile regression is applied to characterize the entire distribution of calorie consumption. This technique has an advantage over the traditional ordinary least square technique. It relaxes the assumption of a constant effect of the explanatory variables over the entire distribution of the dependent variable. These effects are allowed to vary over the entire distribution of dependent variable i.e., in this case the distribution of calorie consumption. The results show that indeed, the responsiveness of calorie consumption to various factors differs across different levels of calorie consumption. A comparison of the quantile regression results with OLS results suggests conclusions and policy suggestions based on OLS results are unlikely to be ideal. Some further light is also shed on the debate on calorie income elasticity as the magnitude is observed to be different for the undernourished and the over nourished households.; no

The effect of household characteristics on living standards in South Africa 1993 - 98: a quantile regression analysis with sample attrition

Maitra, Pushkar; Vahid, Farshid
Fonte: Universidade Nacional da Austrália Publicador: Universidade Nacional da Austrália
Tipo: Working/Technical Paper Formato: 864502 bytes; 350 bytes; application/pdf; application/octet-stream
Português
Relevância na Pesquisa
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This paper examines whether the dismantling of apartheid has resulted in the improvement in the standard of living for the vast majority of South Africans. The study is based on a panel data set from the Kwazulu-Natal province. Despite the best efforts of the interview team, the attrition rate in this panel is around 16%. We find that household income and size in 1993, several community characteristics and survey quality in 1993 significantly affect the probability of attrition. We use weighted quantile regressions to examine the distribution of standards of living, which corrects for the potential bias arising from non-random sample attrition. Our results show that there has been a significant increase in the spread of the distribution of household expenditure of the Non-White households residing in Kwazulu-Natal province. We argue that the stretch to the right of the upper tail of distribution can be attributed to significant increase in returns to primary and high school education, while movement to the left of the lower quantiles can be associated with the increase in the proportion of female headed households and household size.; no

Glass ceiling or sticky floor? Exploring the Australian gender pay gap using quantile regression and counterfactual decomposition methods

Kee, Hiau Joo
Fonte: Universidade Nacional da Austrália Publicador: Universidade Nacional da Austrália
Tipo: Working/Technical Paper Formato: 385979 bytes; 350 bytes; application/pdf; application/octet-stream
Português
Relevância na Pesquisa
66.54%
Using the HILDA survey, this paper analyses Australian gender wage gaps in both public and private sectors across the wage distribution. Quantile Regression (QR) techniques are used to control for various characteristics at different points of the wage distributions. Counterfactual decomposition analysis, adjusted for the QR framework, is utilised to examine if the gap is attributed to differences in gender characteristic, or differing returns between genders. The main finding is that a strong glass ceiling effect is detected only in the private sector. Secondly, the acceleration in the gender gap across the distribution does not vanish even after extensive controls. This suggests that the observed wage gap is a result of differences in returns to genders. By focussing only on the mean gender wage gap, substantial variations of the gap will be hidden.; no

Applied quantile regression: microeconometric, financial, and environmental analyses; Angewandte Quantilsregression: Analysen zur Mikroökonometrie, Finanzmarktforschung und Meteorologie

Schulze, Niels
Fonte: Universidade de Tubinga Publicador: Universidade de Tubinga
Tipo: Dissertação
Português
Relevância na Pesquisa
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In 1978, Roger Koenker and Gilbert Bassett, Jr. introduced a new econometric estimation method and entitled it quantile regression. Since then, many subsequent authors have elaborated and extended the underlying theoretical framework. Other contributions have successfully applied the procedure to a wide range of problems from a variety of scientific branches. This study presents the basic features of quantile regression along with some important properties and a selection of significant extensions and applications. Subsequently, the procedure is used in three new and original empirical regression settings to demonstrate the universality and flexibility of the approach.; Vor einem guten Vierteljahrhundert entwickelten Roger Koenker und Gilbert Bassett Jr. eine neue ökonometrische Schätzmethode und nannten diese "Quantilsregression'. Seit ihrer Veröffentlichung wurden die theoretischen Grundlagen der Methode durch eine Vielzahl von Autoren verbessert und erweitert. Andere Beiträge beschäftigten sich mit der erfolgreichen Anwendung der Schätztechnik in den unterschiedlichsten wissenschaftlichen Disziplinen. Diese Arbeit beginnt mit einer umfassenden Darstellung der grundlegenden Elemente und Eigenschaften der Quantilsregression. Anschliessend wird eine Auswahl wichtiger Erweiterungen sowie Anwendungen präsentiert. Den Abschluß bildet der Einsatz der ökonmetrischen Methode in drei verschiedenen Gebieten...

Evaluating Value-at-Risk models via Quantile Regression

Gaglianone, Wagner Piazza; Lima, Luiz Renato; Linton, Oliver; Smith, Daniel
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /05/2009 Português
Relevância na Pesquisa
66.62%
This paper is concerned with evaluating value at risk estimates. It is well known that using only binary variables, such as whether or not there was an exception, sacrifices too much information. However, most of the specification tests (also called backtests) available in the literature, such as Christoffersen (1998) and Engle and Maganelli (2004) are based on such variables. In this paper we propose a new backtest that does not rely solely on binary variables. It is shown that the new backtest provides a sufficient condition to assess the finite sample performance of a quantile model whereas the existing ones do not. The proposed methodology allows us to identify periods of an increased risk exposure based on a quantile regression model (Koenker & Xiao, 2002). Our theoretical findings are corroborated through a Monte Carlo simulation and an empirical exercise with daily S&P500 time series.

Consistent specification testing of quantile regression models

Delgado, Miguel A.; Domínguez, Manuel A.
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /05/1997 Português
Relevância na Pesquisa
56.42%
This paper introduces a specification testing procedure for quantile regression functions consistent in the direction of nonparametric alternatives. We consider test statistics based on a marked empirical process which does not require to estimate nonparametrically the true model. In general, the tests are not distribution free, but critical values can be consistentIy approximated using a residual based bootstrap. A small Monte Cario experiment shows that the test works fairly well in practice.

Robust Quantile Regression Using L2E

Lane, Jonathan W.
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Thesis; Text Formato: 132 p.; application/pdf
Português
Relevância na Pesquisa
66.91%
Quantile regression, a method used to estimate conditional quantiles of a set of data ( X, Y ), was popularized by Koenker and Bassett (1978). For a particular quantile q , the q th quantile estimate of Y given X = x can be found using an asymmetrically-weighted, absolute-loss criteria. This form of regression is considered to be robust, in that it is less affected by outliers in the data set than least-squares regression. However, like standard L 1 regression, this form of quantile regression can still be affected by multiple outliers. In this thesis, we propose a method for improving robustness in quantile regression through an application of Scott's L 2 Estimation (2001). Theoretic and asymptotic results are presented and used to estimate properties of our method. Along with simple linear regression, semiparametric extensions are examined. To verify our method and its extensions, simulated results are considered. Real data sets are also considered, including estimating the effect of various factors on the conditional quantiles of child birth weight, using semiparametric quantile regression to analyze the relationship between age and personal income, and assessing the value distributions of Major League Baseball players.

Three Essays on Time Series Quantile Regression

Wang, Yini
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
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This dissertation considers quantile regression models with nonstationary or nearly nonstationary time series. The first chapter outlines the thesis and discusses its theoretical and empirical contributions. The second chapter studies inference in quantile regressions with cointegrated variables allowing for multiple structural changes. The unknown break dates and regression coefficients are estimated jointly and consistently. The conditional quantile estimator has a nonstandard limit distribution. A fully modified estimator is proposed to remove the second-order bias and nuisance parameters and the resulting limit distribution is mixed normal. A simulation study shows that the fully modified quantile estimator has good finite sample properties. The model is applied to stock index data from the emerging markets of China and several mature markets. Financial market integration is found in some quantiles of the Chinese stock indices. The third chapter considers predictive quantile regression with a nearly integrated regressor. We derive nonstandard distributions for the quantile regression estimator and t-statistic in terms of functionals of diffusion processes. The critical values are found to depend on both the quantile of interest and the local-to-unity parameter...

Male and female wage functions : a quantile regression analysis using LEED and LFS portuguese databases

Figueiredo, Maria da Conceição; Fontainha, Elsa
Fonte: ISEG – Departamento de Economia Publicador: ISEG – Departamento de Economia
Tipo: Trabalho em Andamento
Publicado em //2015 Português
Relevância na Pesquisa
66.66%
This paper was presented at the 3rd Linked Employer-Employee Data Workshop LEED 2013, June 27-28, Lisbon, Portugal; The research aims to study the distribution of hourly wages for men and women in Portugal, adopting a quantile regression (QR) approach. Two databases are used for the estimation of the wage functions: the Quadros de Pessoal, Linked Employer-Employee Data (QP-LEED) and the Inquérito ao Emprego, Portuguese Labour Force Survey (IE-LFS). Three basic models are considered to explain the hourly wages for men and women: the first model, using each database separately, is estimated adopting education, tenure, potential experience, activity sector, and job as independent variables; the second, using data from QP-LEED, includes additional determinants related to firm (firm size and foreign social capital); and the third, using data from the IE-LFS, includes additional independent variables related to the worker's family (marital status and children). The results indicate that: (i) Regardless of the database used, the quantile regression (QR) shows superiority over OLS approach; (ii) In general, the same model specification estimated using each database - one administrative (QP-LEED), and the other based on a survey (IE-LFS) - present convergent results; (iii) Independently of the database used...

Risk Margin Quantile Function Via Parametric and Non-Parametric Bayesian Quantile Regression

Dong, Alice X. D.; Chan, Jennifer S. K.; Peters, Gareth W.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 11/02/2014 Português
Relevância na Pesquisa
46.94%
We develop quantile regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk margin and an overview of implied capital based on the historical volatility of a general insurers loss portfolio. Two modelling frameworks are considered based around parametric and nonparametric quantile regression models which we develop specifically in this insurance setting. In the parametric quantile regression framework, several models including the flexible generalized beta distribution family, asymmetric Laplace (AL) distribution and power Pareto distribution are considered under a Bayesian regression framework. The Bayesian posterior quantile regression models in each case are studied via Markov chain Monte Carlo (MCMC) sampling strategies. In the nonparametric quantile regression framework, that we contrast to the parametric Bayesian models, we adopted an AL distribution as a proxy and together with the parametric AL model, we expressed the solution as a scale mixture of uniform distributions to facilitate implementation. The models are extended to adopt dynamic mean...

Globally adaptive quantile regression with ultra-high dimensional data

Zheng, Qi; Peng, Limin; He, Xuming
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
46.89%
Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high-dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically pre-specified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high-dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal; Comment: This paper has been withdrawn by the author due to a crucial proof error in Appendix

Partial Functional Linear Quantile Regression for Neuroimaging Data Analysis

Yu, Dengdeng; Kong, Linglong; Mizera, Ivan
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/11/2015 Português
Relevância na Pesquisa
46.93%
We propose a prediction procedure for the functional linear quantile regression model by using partial quantile covariance techniques and develop a simple partial quantile regression (SIMPQR) algorithm to efficiently extract partial quantile regression (PQR) basis for estimating functional coefficients. We further extend our partial quantile covariance techniques to functional composite quantile regression (CQR) defining partial composite quantile covariance. There are three major contributions. (1) We define partial quantile covariance between two scalar variables through linear quantile regression. We compute PQR basis by sequentially maximizing the partial quantile covariance between the response and projections of functional covariates. (2) In order to efficiently extract PQR basis, we develop a SIMPQR algorithm analogous to simple partial least squares (SIMPLS). (3) Under the homoscedasticity assumption, we extend our techniques to partial composite quantile covariance and use it to find the partial composite quantile regression (PCQR) basis. The SIMPQR algorithm is then modified to obtain the SIMPCQR algorithm. Two simulation studies show the superiority of our proposed methods. Two real data from ADHD-200 sample and ADNI are analyzed using our proposed methods.

Flexible quantile regression models : application to the study of the purple sea urchin

Martínez-Silva, Isabel; Roca-Pardiñas, Javier; Lustres-Pérez, Vicente; Lorenzo-Arribas, Altea; Cadarso-Suárez, Carmen
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2013 Português
Relevância na Pesquisa
66.59%
In many applications, it is often of interest to assess the possible relationships between covariates and quantiles of a response variable through a regression model. In some instances, the effects of continuous covariates on the outcome are highly nonlinear. Consequently, appropriate modelling has to take such flexible smooth effects into account. In this work, various flexible quantile regression techniques were reviewed and compared by simulation. Finally, all the techniques were used to construct the overall zone specific reference curves of morphologic measures of sea urchin Paracentrotus lividus (Lamarck, 1816) located in NW Spain.