A non-parametric data envelopment analysis approach for improving energy efficiency of grape production

Grape is one of the world's largest fruit crops with approximately 67.5 million tonnes produced each year and energy is an important element in modern grape productions as it heavily depends on fossil and other energy resources. Efficient use of these energies is a necessary step toward reducing environmental hazards, preventing destruction of natural resources and ensuring agricultural sustainability. Hence, identifying excessive use of energy as well as reducing energy resources is the main focus of this paper to optimize energy consumption in grape production.In this study we use a two-stage methodology to find the association of energy efficiency and performance explained by farmers' specific characteristics. In the first stage a non-parametric Data Envelopment Analysis is used to model efficiencies as an explicit function of human labor, machinery, chemicals, FYM (farmyard manure), diesel fuel, electricity and water for irrigation energies. In the second step, farm specific variables such as farmers' age, gender, level of education and agricultural experience are used in a Tobit regression framework to explain how these factors influence efficiency of grape farming.The result of the first stage shows substantial inefficiency between the grape producers in the studied area while the second stage shows that the main difference between efficient and inefficient farmers was in the use of chemicals, diesel fuel and water for irrigation. The use of chemicals such as insecticides, herbicides and fungicides were considerably less than inefficient ones. The results revealed that the more educated farmers are more energy efficient in comparison with their less educated counterparts. © 2013.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Age and growth in olive ridley seaturtles (Lepidochelys olivacea) from the north-central Pacific: a skeletochronological analysis

The olive ridley is the most abundant seaturtle species in the world but little is known of the demography of this species. We used skeletochronological data on humerus diameter growth changes to estimate the age of North Pacific olive ridley seaturtles caught incidentally by pelagic longline fisheries operating near Hawaii and from dead turtles washed ashore on the main Hawaiian Islands. Two age estimation methods [ranking, correction factor (CF)] were used and yielded age estimates ranging from 5 to 38 and 7 to 24 years, respectively. Rank age-estimates are highly correlated (r = 0.93) with straight carapace length (SCL), CF age estimates are not (r = 0.62). We consider the CF age-estimates as biologically more plausible because of the disassociation of age and size. Using the CF age-estimates, we then estimate the median age at sexual maturity to be around 13 years old (mean carapace size c. 60 cm SCL) and found that somatic growth was negligible by 15 years of age. The expected age-specific growth rate function derived using numerical differentiation suggests at least one juvenile growth spurt at about 10–12 years of age when maximum age-specific growth rates, c. 5 cm SCL year−1, are apparent.

Online bayesian inference in some time-frequency representations of non-stationary processes

The use of Bayesian inference in the inference of time-frequency representations has, thus far, been limited to offline analysis of signals, using a smoothing spline based model of the time-frequency plane. In this paper we introduce a new framework that allows the routine use of Bayesian inference for online estimation of the time-varying spectral density of a locally stationary Gaussian process. The core of our approach is the use of a likelihood inspired by a local Whittle approximation. This choice, along with the use of a recursive algorithm for non-parametric estimation of the local spectral density, permits the use of a particle filter for estimating the time-varying spectral density online. We provide demonstrations of the algorithm through tracking chirps and the analysis of musical data.

Choosing Smoothness Parameters for Smoothing Splines by Minimizing and Estimate of Risk

Smoothing splines are a popular approach for non-parametric regression problems. We use periodic smoothing splines to fit a periodic signal plus noise model to data for which we assume there are underlying circadian patterns. In the smoothing spline methodology, choosing an appropriate smoothness parameter is an important step in practice. In this paper, we draw a connection between smoothing splines and REACT estimators that provides motivation for the creation of criteria for choosing the smoothness parameter. The new criteria are compared to three existing methods, namely cross-validation, generalized cross-validation, and generalization of maximum likelihood criteria, by a Monte Carlo simulation and by an application to the study of circadian patterns. For most of the situations presented in the simulations, including the practical example, the new criteria out-perform the three existing criteria.

Metodologia para a suavização de dados biomecanicos por função não parametrica ponderada local robusta

Universidade Estadual de Campinas. Faculdade de Educação Física

Gender differences in the relationship between depression and suicidal ideation in young adolescents

Objective: This study examined the risk relationship between depressive symptomatology and suicidal ideation for young adolescent males and females. Method: A large cohort of students in their first year of high school completed the Center for Epidemiological Studies Depression Scale (CES-D) and the Adolescent Suicide Questionnaire. The risk relationship between depressive symptomatology and suicidal ideation was modelled using non-parametric kernel-smoothing techniques. Results: Suicidal ideation was more frequently reported by females compared with males which was partly explained by females having higher mean depression scores. At moderate levels of depression females also had a significantly higher risk of suicidal ideation compared with males and this increased risk contributed to the overall higher levels of female ideation. Conclusions: The risk relationship between depressive symptomatology and suicidal ideation is different for young adolescent males and females. The results indicate that moderate levels of depressive symptomatology can be associated with suicidal ideation (especially among young females) and that for these young people a suicide risk assessment is required.

The reasons behind the progression in PISA scores: An education production function approach using semi-parametric techniques

Research Masters

Estimation of parametric and nonparametric models for univariate claim severity distributions - an approach using R

This paper presents an analysis of motor vehicle insurance claims relating to vehicle damage and to associated medical expenses. We use univariate severity distributions estimated with parametric and non-parametric methods. The methods are implemented using the statistical package R. Parametric analysis is limited to estimation of normal and lognormal distributions for each of the two claim types. The nonparametric analysis presented involves kernel density estimation. We illustrate the benefits of applying transformations to data prior to employing kernel based methods. We use a log-transformation and an optimal transformation amongst a class of transformations that produces symmetry in the data. The central aim of this paper is to provide educators with material that can be used in the classroom to teach statistical estimation methods, goodness of fit analysis and importantly statistical computing in the context of insurance and risk management. To this end, we have included in the Appendix of this paper all the R code that has been used in the analysis so that readers, both students and educators, can fully explore the techniques described

Multiple-scale hydrological and geophysical data integration through non-linear Bayesian sequential simulation

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the scale of a field site represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed downscaling procedure based on a non-linear Bayesian sequential simulation approach. The main objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity logged at collocated wells and surface resistivity measurements, which are available throughout the studied site. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariatekernel density function. Then a stochastic integration of low-resolution, large-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities is applied. The overall viability of this downscaling approach is tested and validated by comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure allows obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Forecasting with missing data: Application to a real case

This paper presents a comparative analysis of linear and mixed modelsfor short term forecasting of a real data series with a high percentage of missing data. Data are the series of significant wave heights registered at regular periods of three hours by a buoy placed in the Bay of Biscay.The series is interpolated with a linear predictor which minimizes theforecast mean square error. The linear models are seasonal ARIMA models and themixed models have a linear component and a non linear seasonal component.The non linear component is estimated by a non parametric regression of dataversus time. Short term forecasts, no more than two days ahead, are of interestbecause they can be used by the port authorities to notice the fleet.Several models are fitted and compared by their forecasting behavior.

Parametric Approach to Blind Deconvolution of Nonlinear Channels

A parametric procedure for the blind inversion of nonlinear channels is proposed, based on a recent method of blind source separation in nonlinear mixtures. Experiments show that the proposed algorithms perform efficiently, even in the presence of hard distortion. The method, based on the minimization of the output mutual information, needs the knowledge of log-derivative of input distribution (the so-called score function). Each algorithm consists of three adaptive blocks: one devoted to adaptive estimation of the score function, and two other blocks estimating the inverses of the linear and nonlinear parts of the channel, (quasi-)optimally adapted using the estimated score functions. This paper is mainly concerned by the nonlinear part, for which we propose two parametric models, the first based on a polynomial model and the second on a neural network, while [14, 15] proposed non-parametric approaches.

Les multiples trajectoires d’activité physique supervisée et non supervisée chez les enfants du primaire au Québec : un modèle écologique

Contexte : L’activité physique est une composante centrale du développement physique, psychologique et social de l'enfant, particulièrement au sein d'une société où l'impact de la sédentarité et de l'obésité devient de plus en plus important. Cependant, les trajectoires d’activité physique hors école et leurs déterminants sont peu étudiés et les connaissances sur ce sujet sont limitées. Il est également notoire que les types d’activité physique sont rarement pris en considération. Objectif : Ce mémoire a pour but (a) de déterminer les trajectoires de pratique d’activité physique au cours du développement des enfants (b) de valider l’association entre l’activité physique supervisée et l’activité non supervisée et (c) d’identifier les déterminants au niveau du quartier, de la famille et des caractéristiques individuelles associés aux trajectoires de pratique d’activité physique supervisée et non supervisée. Participants : 1 814 enfants (51% garçons) nés en 1998 ayant participé à l’Étude Longitudinale du Développement des Enfants du Québec (ELDEQ). Les données récoltées proviennent uniquement de leur mère. Mesures : La fréquence de l’activité physique supervisée et non supervisée a été mesurée à quatre reprises alors que les enfants étaient âgés entre 5 et 8 ans. Les déterminants ainsi que les variables contrôles ont été mesurés alors que les enfants avaient 4 ou 5 ans. Résultats : Trois trajectoires d’activité physique supervisée et non supervisée ont été identifiées. Les résultats suggèrent que les trajectoires d’activité physique supervisée, représentant respectivement 10%, 55.3% et 34.7% de la population, sont relativement stables même si elles subissent une légère augmentation avec le temps. Des trois trajectoires d’activité physique non supervisée représentant respectivement 14.1%, 28.1% et 57.8% de la population, une augmente considérablement avec le temps alors iv que les deux autres sont stables. Ces deux séries de trajectoires ne sont pas associées significativement entre elles. L’éducation de la mère, l’entraide dans le quartier de résidence ainsi que la prosocialité des enfants déterminent les deux types d’activité physique. La suffisance de revenu et la pratique sportive de la mère sont associées seulement aux trajectoires d’activité physique supervisée. La famille intacte discrimine l’appartenance aux trajectoires d’activité physique non supervisée. Conclusion : Premièrement, la pratique de l’activité physique est relativement stable entre 5 et 8 ans. Deuxièmement, l’activité physique supervisée ainsi que l’activité physique non supervisée sont deux pratiques qui se développent différemment et qui possèdent leurs propres déterminants. Troisièmement, une approche écologique permet de mieux saisir la complexité de ces deux processus.