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El análisis espectral, es el proceso técnico de la descomposición de una señal compleja en partes más simples. Muchos procesos físicos se describen mejor como una suma de muchas componentes de frecuencias individuales, alternativamente una señal puede dividirse en segmentos cortos y el análisis del espectro puede aplicarse a estos segmentos individuales. Desde el punto de vista del estudio de olas marinas, el espectro juega un rol fundamental y es interpretado como la energía presente en la serie de tiempo durante el periodo. Este estudio se basa en el análisis del espectro asociado a datos del mar del Norte en el ano de 1999 en la plataforma North Alwyn almacenados en la Universidad Heriot-Watt de Edimburgo, separados en periodos de 20 minutos, el total a estudiar son 244 periodos. Para ello, se determinó que existe información que no aporta al problema, representa ruido y es el 3% de la energía, la cual no fue tomada para el estudio. Se dividió el espectro en 12 subintervalos y se ajustaron modelos autorregresivos bajo el esquema de Box-Jenskin and Reinsel, estudiados estos hasta encontrar la mejor aproximación posible para cada subintervalo mediante las propiedades estadísticas obtenidas por cada modelo, junto con los pronósticos respectivos. // ABSTRACT: Spectral analysis is the technical process of a complex signal decomposition into simpler parts. Many physical processes are better described as a sum of many individual frequency components; alternatively a signal can be divided into short segments and the spectrum analysis can be applied to these individual segments. From the point of view of the study of Waves Sea, the spectrum plays a vital role and it is interpreted as the energy in the time series during the period. This study is based on analysis of the spectrum associated to data from the North Sea in the year of 1999, taken in North Alwyn platform, stored in the Heriot-Watt University in Edinburgh, separated in periods of 20 minutes. The total of the studied periods is 244. Thus, it was determined that there is information that does not add information to the problem, and it represents noise, which is the 3% of energy and it was not taken for the study. The spectrum was divided into 12 subintervals. Autoregressive models were fitted under the scheme Box-Jenskin and Reinsel. The model was studied until the best possible approximation for each subinterval was found by the statistical properties obtained by each model, together with the respective forecasts.

Test-day records of milk yields from the first three lactations were used to verify consequences of incorporation of lactation with non-conventional drying-off causes in genetic evaluation of Gyr dairy cattle. The first file (File1) was composed of test-day records of lactations with conventional drying-off causes. In the second file (File2), the test-day records of lactations with non-conventional drying-off causes were included, such as drying-off by death or separation of the calf, disease, death or sale of cow, and removal of cow from milking control. Data were analyzed by mixed models, using an autoregressive process to adjust the random effects of long-term and short-term environment. The inclusion of test-day records from lactations with non-conventional drying-off causes increased the number of controls by 12% and the number of cows by 18%, and it generated an increase in the estimates of variance components. These results impacted estimates of heritability, calculated at 0.32, 0.28 and 0.26 for the first, second and third lactations in File1, respectively, while in File2 they were 0.34, 0.30 and 0.28 to the same lactation order. The estimated accuracy of predicted breeding values in File2 were higher in relation to File1, but the Pearson and Spearman correlations between predicted breeding values of animals in the two files were high. Lactations with non-conventional drying-off causes can be incorporated to the routines of genetic evaluation of Gyr dairy cattle, because they provide higher estimates of heritability and accuracy of breeding values, which could raise expectations of genetic gains with selection.

This work was carried out to evaluate the statistical properties of eight nonlinear models used to predict nitrogen mineralization in soils of the Southern Minas Gerais State, Brazil. The parameter estimations for nonlinear models with and without structure of autoregressive errors was made by the least squares method. First, a structure of second order autoregressive errors, AR(2) was considered for all nonlinear models and then the significance of the autocorrelation parameters was verified. Among the models, the Juma presented an autocorrelation of second order, and the model of Broadbent presented one of first order. In summary, these models presented significant autocorrelation parameters. To estimate the parameters of nonlinear models, the SAS procedure MODEL was used (SAS). The comparison of the models was made by measuring the fitted parameters: adjusted R-square, mean square error and mean predicted error. The Juma model with AR(2) best fitted for nitrogen mineralization without liming, followed by Cabrera, Stanford & Smith without autoregressive errors, for both with and without soil acidity correction.Este trabalho teve por objetivo avaliar o grau do ajuste de oito modelos não lineares apresentados na literatura, utilizados para descrever a mineralização do nitrogênio em latossolo do sul de Minas Gerais incubado durante 28 semanas. A estimação dos parâmetros para os modelos de regressão não linear sem e com estrutura de erros autorregressivos foi feita pelo método de mínimos quadrados. A princípio, considerou-se para todos os modelos não lineares uma estrutura de erros autorregressivos de segunda ordem, AR(2) e, em seguida, verificou-se a significância dos parâmetros de autocorrelação. Apenas o modelo de Juma apresentou autocorrelação de segunda ordem, e o modelo de Broadbent apresentou autocorrelação de primeira ordem, ou seja, apenas estes modelos apresentaram parâmetros de autocorrelação significativos. Para estimação dos parâmetros dos modelos não lineares, utilizou-se o procedimento MODEL (SAS®). A comparação dos modelos foi feito por meio de critérios da qualidade do ajuste (coeficiente de determinação ajustado, quadrado médio do resíduo e erro de predição médio). O modelo de melhor ajuste foi o de Juma com AR(2), para a mineralização de N sem calagem, seguido pelos modelos de Cabrera, Stanford & Smith sem estrutura de erros autorregressivos, tanto para os dados com, quanto para aqueles obtidos sem a correção da acidez do solo.

The existence or otherwise of density dependence within a population can have important implications for the management of that population. Here, we use estimates of abundance obtained from annual aerial counts on the major breeding grounds of a variety of North American duck species and use a state space model to separate the observation and ecological system processes. This state space approach allows us to impose a density dependence structure upon the true underlying population rather than on the estimates and we demonstrate the improved robustness of this procedure for detecting density dependence in the population. We adopt a Bayesian approach to model fitting, using Markov chain Monte Carlo (MCMC) methods and use a reversible jump MCMC scheme to calculate posterior model probabilities which assign probabilities to the presence of density dependence within the population, for example. We show how these probabilities can be used either to discriminate between models or to provide model-averaged predictions which fully account for both parameter and model uncertainty.