2013: Vol. 37, Núm. 1
http://hdl.handle.net/2099/13737
2021-10-28T08:24:11ZModelling "calçots" (Allium cepa L.) growth by Gompertz function
http://hdl.handle.net/2099/13771
Modelling "calçots" (Allium cepa L.) growth by Gompertz function
Simó, Joan; Plans, Marçal; Casañas, Francesc; Sabaté, Jose
"Calçots" are the second-year resprouts of the "Ceba Blanca Tardana de Lleida" landrace of onions. The evolution of three "calçots" populations has been modeled to help farmers to plan the optimal time to harvest. Four different models that essentially differ in the type of distribution of the fitting Gompertz function parameters (lag time, maximum growth rate and the maximum attainable number of commercial size "calçots") have been tested. The model that considers a multinomial distribution of the fitting parameters showed the best agreement with the experimental data
2013-09-10T11:18:18ZSimó, JoanPlans, MarçalCasañas, FrancescSabaté, Jose"Calçots" are the second-year resprouts of the "Ceba Blanca Tardana de Lleida" landrace of onions. The evolution of three "calçots" populations has been modeled to help farmers to plan the optimal time to harvest. Four different models that essentially differ in the type of distribution of the fitting Gompertz function parameters (lag time, maximum growth rate and the maximum attainable number of commercial size "calçots") have been tested. The model that considers a multinomial distribution of the fitting parameters showed the best agreement with the experimental dataFlexible quantile regression models: application to the study of the purple sea urchin
http://hdl.handle.net/2099/13770
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
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
2013-09-10T11:16:51ZMartínez-Silva, IsabelRoca-Pardiñas, JavierLustres-Pérez, VicenteLorenzo-Arribas, AlteaCadarso-Suárez, CarmenIn 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 SpainNew insights into evaluation of regression models through a decomposition of the prediction errors: application to near-infrared spectral data
http://hdl.handle.net/2099/13769
New insights into evaluation of regression models through a decomposition of the prediction errors: application to near-infrared spectral data
Sánchez Rodríguez, María Isabel; Sánchez-López, Elena; Caridad, Jose María; Marinas, Alberto; Marinas, Jose Maria; Urbano, Francisco José
This paper analyzes the performance of linear regression models taking into account usual criteria such as the number of principal components or latent factors, the goodness of fit or the predictive capability. Other comparison criteria, more common in an economic context, are also considered: the degree of multicollinearity and a decomposition of the mean squared error of the prediction which determines the nature, systematic or random, of the prediction errors. The applications use real data of extra-virgin oil obtained by near-infrared spectroscopy. The high dimensionality of the data is reduced by applying principal component analysis and partial least squares analysis. A possible improvement of these methods by using cluster analysis or the information of the relative maxima of the spectrum is investigated. Finally, obtained results are generalized via cross-validation and bootstrapping.
2013-09-10T11:14:45ZSánchez Rodríguez, María IsabelSánchez-López, ElenaCaridad, Jose MaríaMarinas, AlbertoMarinas, Jose MariaUrbano, Francisco JoséThis paper analyzes the performance of linear regression models taking into account usual criteria such as the number of principal components or latent factors, the goodness of fit or the predictive capability. Other comparison criteria, more common in an economic context, are also considered: the degree of multicollinearity and a decomposition of the mean squared error of the prediction which determines the nature, systematic or random, of the prediction errors. The applications use real data of extra-virgin oil obtained by near-infrared spectroscopy. The high dimensionality of the data is reduced by applying principal component analysis and partial least squares analysis. A possible improvement of these methods by using cluster analysis or the information of the relative maxima of the spectrum is investigated. Finally, obtained results are generalized via cross-validation and bootstrapping.A note on the Fisher information matrix for the skew-generalized-normal model
http://hdl.handle.net/2099/13758
A note on the Fisher information matrix for the skew-generalized-normal model
Arellano-Valle, Reinaldo B.; Gómez, Héctor W.; Salinas, Hugo S.
In this paper, the exact form of the Fisher information matrix for the skew-generalized normal (SGN) distribution is determined. The existence of singularity problems of this matrix for the skew-normal and normal particular cases is investigated. Special attention is given to the asymptotic properties of the MLEs under the skew-normality hypothesis.
2013-09-09T13:08:14ZArellano-Valle, Reinaldo B.Gómez, Héctor W.Salinas, Hugo S.In this paper, the exact form of the Fisher information matrix for the skew-generalized normal (SGN) distribution is determined. The existence of singularity problems of this matrix for the skew-normal and normal particular cases is investigated. Special attention is given to the asymptotic properties of the MLEs under the skew-normality hypothesis.Improved entropy based test of uniformity using ranked set samples
http://hdl.handle.net/2099/13757
Improved entropy based test of uniformity using ranked set samples
Mahdizadeh, M.; Arghami, N.R.
Ranked set sampling (RSS) is known to be superior to the traditional simple random sampling
(SRS) in the sense that it often leads to more efficient inference procedures. Basic version of RSS
has been extensively modified to come up with schemes resulti
ng in more accurate estimators of
the population attributes. Multistage ranked set sampling
(MSRSS) is such a variation surpassing
RSS. Entropy has been instrumental in constructing criteria for fitting of parametric models to the data. The goal of this article is to develop tests of uniformity based on sample entropy under RSS and MSRSS designs. A Monte Carlo simulation study is carried out to compare the power of the proposed tests under several alternative distributions with the ordinary test based on SRS. The results report that the new entropy tests have higher power than the original one for nearly all sample sizes and under alternatives considered.
2013-09-09T13:07:02ZMahdizadeh, M.Arghami, N.R.Ranked set sampling (RSS) is known to be superior to the traditional simple random sampling
(SRS) in the sense that it often leads to more efficient inference procedures. Basic version of RSS
has been extensively modified to come up with schemes resulti
ng in more accurate estimators of
the population attributes. Multistage ranked set sampling
(MSRSS) is such a variation surpassing
RSS. Entropy has been instrumental in constructing criteria for fitting of parametric models to the data. The goal of this article is to develop tests of uniformity based on sample entropy under RSS and MSRSS designs. A Monte Carlo simulation study is carried out to compare the power of the proposed tests under several alternative distributions with the ordinary test based on SRS. The results report that the new entropy tests have higher power than the original one for nearly all sample sizes and under alternatives considered.The normal distribution in some constrained sample spaces
http://hdl.handle.net/2099/13756
The normal distribution in some constrained sample spaces
Mateu-Figueras, Glòria; Pawlowsky-Glahn, Vera; Juan José, Egozcue
Phenomena with a constrained sample space appear frequently in practice. This is the case, for example, with strictly positive data, or with compositional data, such as percentages or proportions. If the natural measure of difference is not the absolute one, simple algebraic properties show that it is more convenient to work with a geometry different from the usual Euclidean geometry in real space, and with a measure different from the usual Lebesgue measure, leading to alternative models that better fit the phenomenon under study. The general approach is presented and illustrated using the normal distribution, both on the positive real line and on the D-part simplex. The original ideas of McAlister in his introduction to the lognormal distribution in 1879, are recovered and updated.
2013-09-09T10:19:46ZMateu-Figueras, GlòriaPawlowsky-Glahn, VeraJuan José, EgozcuePhenomena with a constrained sample space appear frequently in practice. This is the case, for example, with strictly positive data, or with compositional data, such as percentages or proportions. If the natural measure of difference is not the absolute one, simple algebraic properties show that it is more convenient to work with a geometry different from the usual Euclidean geometry in real space, and with a measure different from the usual Lebesgue measure, leading to alternative models that better fit the phenomenon under study. The general approach is presented and illustrated using the normal distribution, both on the positive real line and on the D-part simplex. The original ideas of McAlister in his introduction to the lognormal distribution in 1879, are recovered and updated.