The results of a two-year research project designed to develop techniques for closed cycle production of bigeye scad (Selar crumenophthalmus) are described. Broodstock fish were captured approximately 5 nautical miles east-southeast of Key Biscayne Florida in waters 100 to 400 feet deep. A 28-ton, eight tank, recirculating aquaculture system (RAS) was designed, built and used for broodstock maturation and growout. Between June and October of 2009, thirty-six volitional spawning events were recorded during a five-month spawning season. A larval rearing protocol focused on modified greenwater techniques using microalgae (Isochrysis galbana and Nannochlopsis oculata) at total concentrations of 400,000 cells ml-1 and rotifers (Brachionus plicatilis) at densities of 30 to 50 rotifers ml-1 was developed. A larval rearing trial using this protocol produced 1,940 fully weaned, 45 dph fingerling bigeye scad with an average length of 38.8 ml and average weight of 1.3 g. Growout trials conducted from 45 dph to four and a half months post hatch were conducted. During growout trials fish were fed ad libitum twice a day using commercially available feed with 44% crude protein and 18% crude fat. Growout trials resulted in an average survival rate of 88%, absolute growth rates (AGR) of 28.23 to 30.26 g, and feed conversion ratios (FCRs) between 1.17 and 1.38. Results from an informal survey of local recreational fishing interests indicate that there is a large potential live-bait market for bigeye scad. Overall results from these trials show that bigeye scad aquaculture is technically feasible and suggests that the species is strong candidate for commercialization.

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Dans cette thèse nous nous intéressons aux problèmes de la sélection de variables en régression linéaire. Ces travaux sont en particulier motivés par les développements récents en génomique, protéomique, imagerie biomédicale, traitement de signal, traitement d’image, en marketing, etc… Nous regardons ce problème selon les deux points de vue fréquentielle et bayésienne.Dans un cadre fréquentiel, nous proposons des méthodes pour faire face au problème de la sélection de variables, dans des situations pour lesquelles le nombre de variables peut être beaucoup plus grand que la taille de l’échantillon, avec présence possible d’une structure supplémentaire entre les variables, telle qu’une forte corrélation ou un certain ordre entre les variables successives. Les performances théoriques sont explorées ; nous montrons que sous certaines conditions de régularité, les méthodes proposées possèdent de bonnes propriétés statistiques, telles que des inégalités de parcimonie, la consistance au niveau de la sélection de variables et la normalité asymptotique.Dans un cadre bayésien, nous proposons une approche globale de la sélection de variables en régression construite sur les lois à priori g de Zellner dans une approche similaire mais non identique à celle de Liang et al. (2008) Notre choix ne nécessite aucune calibration. Nous comparons les approches de régularisation bayésienne et fréquentielle dans un contexte peu informatif où le nombre de variables est presque égal à la taille de l’échantillon. / We are interested in variable sélection in linear régression models. This research is motivated by recent development in microarrays, proteomics, brain images, among others. We study this problem in both frequentist and bayesian viewpoints.In a frequentist framework, we propose methods to deal with the problem of variable sélection, when the number of variables is much larger than the sample size with a possibly présence of additional structure in the predictor variables, such as high corrélations or order between successive variables. The performance of the proposed methods is theoretically investigated ; we prove that, under regularity conditions, the proposed estimators possess statistical good properties, such as Sparsity Oracle Inequalities, variable sélection consistency and asymptotic normality.In a Bayesian Framework, we propose a global noninformative approach for Bayesian variable sélection. In this thesis, we pay spécial attention to two calibration-free hierarchical Zellner’s g-priors. The first one is the Jeffreys prior which is not location invariant. A second one avoids this problem by only considering models with at least one variable in the model. The practical performance of the proposed methods is illustrated through numerical experiments on simulated and real world datasets, with a comparison betwenn Bayesian and frequentist approaches under a low informative constraint when the number of variables is almost equal to the number of observations.

Šiame darbe susisteminti ir palyginti pagrindiniai grunto standumo koeficientų nustatymo metodai. Aprašoma metodika, kaip iš grunto santykinio tankio galima apskaičiuoti dinaminį bei statinį tamprumo modulius. Pateikiamos šių grunto parametrų koreliacinės priklausomybės. Eksperimentiniais tyrimais įrodyta, kad silpnuose gruntuose egzistuoja netiesinė grunto tamprumo modulio priklausomybė nuo šalia grunto esančios priekrovos. Priekrovą traktuojant kaip tiesiškai priklausančią nuo gylio, išvesta tamprumo modulio silpnuose gruntuose priklausomybė nuo gylio. Rezervuarų skaičiavimui buvo pasinaudota 2 panašių konstrukcijų skaičiavimo metodikomis (tunelių metodika bei vamzdžių m.). Taip pat pasinaudota СНиП 2.06.09-84 tunelių projektavimo nurodymais. Konstrukcija sumodeliuota projektavimo programomis Robot Structural Analysis Pro, SCAD, Plaxis 2D. Taip pat pateiktas patobulintas modelis, atramas išskirstant į ploto vienetą, autoriaus nuomone, tinkamesnėmis plonasienėms mažo standumo konstrukcijoms. Skaičiuotiniuose modeliuose įvertinami visi pagrindiniai rezervuarų konstrukciniai elementai: galiniai kupolai, lengvosios sąstandos, špangautai. Laboratoriniais bandymais nustatytos ir įvertintos polimerinio kompozito konstrukcinės savybės: virtualus tamprumo modulis, Puasono koeficientas, valkšnumo koeficientas bei senėjimo faktorius. Išvadose apibendrinami rezultatai, taip pat suformuojami pasiūlymai, naudingi tolesniems tiriamiesiems darbams, bei panašių konstrukcijų... [toliau žr. visą tekstą] / Main methods to determine subgrade reaction is systemized in this master thesis work. Determination of static and dynamic deformation modulus, when relative soil density is known, are also familiarized. Correlation between the parameters is given in section 2. Experiments were made to prove that there is a link between deformation modulus and surcharge. If surcharge depends linearly from depth, then a function was created to predict deformation modulus values in weak soil when depth varies. 2 similar construction (pipes and tunnels) methods were used to design a buried tank. Design model was created by these design programs: Robot Structural Analysis Pro, SCAD, and PLAXIS 2D. An additional tank modelling method has been suggested by the author. Supports were assigned in plane instead of in a straight line. The updated model seems to better fit for low stiffness construction materials like GRP composites. In these design models all common tank structural elements were included: longitude domes, light and heavy stiffeners, orthotropic material. Main design parameters like virtual elastic modulus, Poisson ratio, creep factor, factor of durability. Results are summarized in conclusions. Suggestions were provided to help future researchers and designers with this kind of problems: structural design of composite polymer structures, design of buried thin-shelled tanks and evaluation of deformation modulus.

Experimental design has applications in many fields, from medicine to manufacturing. Incorporating statistics into both the planning and analysis stages of the experiment will ensure that appropriate data are collected to allow for meaningful analysis and interpretation of the results. If the number of factors of interest is very large, or if the experimental runs are very expensive, then a supersaturated design (SSD) can be used for factor screening. These designs have n runs and k > n - 1 factors, so there are not enough degrees of freedom to allow estimation of all of the main effects. This paper will first review some of the current techniques for the construction and analysis of SSDs, as well as the analysis challenges inherent to SSDs. Analysis techniques of Sure Independence Screening (SIS) and Iterative Sure Independence Screening (ISIS) are discussed, and their applications for SSDs are explored using simulation, in combination with the Smoothly Clipped Absolute Deviation (SCAD) approach for down-selecting and estimating the effects.

Selection of variables and estimation of regression coefficients in datasets with the number of variables exceeding the number of observations consti- tutes an often discussed topic in modern statistics. Today the maximum penalized likelihood method with an appropriately selected function of the parameter as the penalty is used for solving this problem. The penalty should evaluate the benefit of the variable and possibly mitigate or nullify the re- spective regression coefficient. The SCAD and LASSO penalty functions are popular for their ability to choose appropriate regressors and at the same time estimate the parameters in a model. This thesis presents an overview of up to date results in the area of characteristics of estimates obtained by using these two methods for both small number of regressors and multidimensional datasets in a normal linear model. Due to the fact that the amount of pe- nalty and therefore also the choice of the model is heavily influenced by the tuning parameter, this thesis further discusses its selection. The behavior of the LASSO and SCAD penalty functions for different values and possibili- ties for selection of the tuning parameter is tested with various numbers of regressors on simulated datasets.

We propose to extend the use of penalized likelihood based variable selection methods to hierarchical generalized linear models (HGLMs) for jointly modellingboth the mean and variance structures. We are interested in applying these newmethods on multilevel structured data, hence we assume a two-level hierarchical structure, with subjects nested within groups. We consider a generalized linearmixed model (GLMM) for the mean, with a structured dispersion in the formof a generalized linear model (GLM). In the first instance, we model the varianceof the random effects which are present in the mean model, or in otherwords the variation between groups (between-level variation). In the second scenario,we model the dispersion parameter associated with the conditional varianceof the response, which could also be thought of as the variation betweensubjects (within-level variation). To do variable selection, we use the smoothlyclipped absolute deviation (SCAD) penalty, a penalized likelihood variable selectionmethod, which shrinks the coefficients of redundant variables to 0 and at thesame time estimates the coefficients of the remaining important covariates. Ourmethods are likelihood based and so in order to estimate the fixed effects in ourmodels, we apply iterative procedures such as the Newton-Raphson method, inthe form of the LQA algorithm proposed by Fan and Li (2001). We carry out simulationstudies for both the joint models for the mean and variance of the randomeffects, as well as the joint models for the mean and dispersion of the response,to assess the performance of our new procedures against a similar process whichexcludes variable selection. The results show that our method increases both theaccuracy and efficiency of the resulting penalized MLEs and has 100% successrate in identifying the zero and non-zero components over 100 simulations. Forthe main real data analysis, we use the Health Survey for England (HSE) 2004dataset. We investigate how obesity is linked to several factors such as smoking,drinking, exercise, long-standing illness, to name a few. We also discover whetherthere is variation in obesity between individuals and between households of individuals,as well as test whether that variation depends on some of the factorsaffecting obesity itself.

A family of concave penalties, including the smoothly clipped absolute deviation (SCAD) and minimax concave penalties (MCP), has been shown to have attractive properties in variable selection. The computation of concave penalized solutions, however, is a difficult task. We propose a majorization minimization by coordinate descent (MMCD) algorithm to compute the solutions of concave penalized generalized linear models (GLM). In contrast to the existing algorithms that uses local quadratic or local linear approximation of the penalty, the MMCD majorizes the negative log-likelihood by a quadratic loss, but does not use any approximation to the penalty. This strategy avoids the computation of scaling factors in iterative steps, hence improves the efficiency of coordinate descent. Under certain regularity conditions, we establish the theoretical convergence property of the MMCD algorithm. We implement this algorithm in a penalized logistic regression model using the SCAD and MCP penalties. Simulation studies and a data example demonstrate that the MMCD works sufficiently fast for the penalized logistic regression in high-dimensional settings where the number of covariates is much larger than the sample size. Grouping structure among predictors exists in many regression applications. We first propose an l2 grouped concave penalty to incorporate such group information in a regression model. The l2 grouped concave penalty performs group selection and includes group Lasso as a special case. An efficient algorithm is developed and its theoretical convergence property is established under certain regularity conditions. The group selection property of the l2 grouped concave penalty is desirable in some applications; while in other applications selection at both group and individual levels is needed. Hence, we propose an l1 grouped concave penalty for variable selection at both individual and group levels. An efficient algorithm is also developed for the l1 grouped concave penalty. Simulation studies are performed to evaluate the finite-sample performance of the two grouped concave selection methods. The new grouped penalties are also used in analyzing two motivation datasets. The results from both the simulation and real data analyses demonstrate certain benefits of using grouped penalties. Therefore, the proposed concave group penalties are valuable alternatives to the standard concave penalties.

No description available.

We are interested in variable sélection in linear régression models. This research is motivated by recent development in microarrays, proteomics, brain images, among others. We study this problem in both frequentist and bayesian viewpoints.In a frequentist framework, we propose methods to deal with the problem of variable sélection, when the number of variables is much larger than the sample size with a possibly présence of additional structure in the predictor variables, such as high corrélations or order between successive variables. The performance of the proposed methods is theoretically investigated ; we prove that, under regularity conditions, the proposed estimators possess statistical good properties, such as Sparsity Oracle Inequalities, variable sélection consistency and asymptotic normality.In a Bayesian Framework, we propose a global noninformative approach for Bayesian variable sélection. In this thesis, we pay spécial attention to two calibration-free hierarchical Zellner's g-priors. The first one is the Jeffreys prior which is not location invariant. A second one avoids this problem by only considering models with at least one variable in the model. The practical performance of the proposed methods is illustrated through numerical experiments on simulated and real world datasets, with a comparison betwenn Bayesian and frequentist approaches under a low informative constraint when the number of variables is almost equal to the number of observations.