O interesse pela quantificação da biomassa florestal vem crescendo muito nos últimos anos, sendo este crescimento relacionado diretamente ao potencial que as florestas tem em acumular carbono atmosférico na sua biomassa. A biomassa florestal pode ser acessada diretamente, por meio de inventário, ou através de modelos empíricos de predição. A construção de modelos de predição de biomassa envolve a mensuração das variáveis e o ajuste e seleção de modelos estatísticos. A partir de uma amostra destrutiva de de 200 indivíduos de dez essências florestais distintas advindos da região de Linhares, ES., foram construídos modelos de predição empíricos de biomassa aérea visando futuro uso em projetos de reflorestamento. O processo de construção dos modelos consistiu de uma análise das técnicas de obtenção dos dados e de ajuste dos modelos, bem como de uma análise dos processos de seleção destes a partir do critério de Informação de Akaike (AIC). No processo de obtenção dos dados foram testadas a técnica volumétrica e a técnica gravimétrica, a partir da coleta de cinco discos de madeira por árvore, em posições distintas no lenho. Na técnica gravimétrica, estudou-se diferentes técnicas de composição do teor de umidade dos discos para determinação da biomassa...
The minor hemoglobin components, hemoglobin AIa+b and hemoglobin AIc, were measured in the 10% youngest and 10% oldest erythrocytes of 15 normal and 14 diabetic subjects. Erythrocyte fractions were obtained by centrifugation in isopyknic concentrations of dextran: 28.5% of 40,000-mol wt dextran yeilded the 10% lightest of young cells, and 30.5% dextran provided the 10% heaviest or old erythrocytes. Both normal and diabetic erythrocytes contain increased amounts of Hb AIa+b and Hb AIc in old as compared to young cells. In normal subjects, young cells contained 1.2+/-0.2%, and old cells contained 1.8+/-0.4% Hb AIa+b. Corresponding values for diabetic cells were 1.7+/-0.6 and 2.6+/-0.9%. Hb AIc increased from 3.1+/-0.8 to 6.0+/-1.1% in normals and from 5.1+/-2.1 to 10.1+/-3.7% in diabetics. The results indicate that both cell age and diabetes are significant determinants of the amounts of Hb AIa+b and Hb AIc.
The formation of hemoglobin AIc was studied in intact human erythrocytes in vitro. Satisfactory methods were developed for maintaining erythrocytes under physiologic conditions for greater than 8 d with less than 10% hemolysis. Hemoglobin AIc levels were determined chromatographically on erythrocyte hemolysates after removal of reversible components by incubation for 6 h at 37 degree C. Hemoglobin AIc concentration was found to increase linearly with time during 8 d of incubation. The rate of formation of hemoglobin AIc increased linearly as glucose concentration was increased from 40 to 1,000 mg/dl. Deoxyhemoglobin was glycosylated twice as rapidly as oxyhemoglobin. The rate of hemoglobin AIc formation was further increased by elevated 2,3-diphosphoglycerate levels, an effect that was most marked with deoxyhemoglobin. We conclude that the nonenzymatic glycosylation of hemoglobin is influenced by factors other than glucose, including oxygen tension and 2,3-diphosphoglycerate levels.
Adult diabetic mice (C57Bl/KsJ--db/db) have increased amounts of a minor hemoglobin in their peripheral blood compared to wild-type (+/+) mice. This increase is analogous to the 2-fold increase of a glycohemoglobin with similar chromatographic mobility (Hb AIc) seen in the blood of patients with diabetes mellitus. Although the exact chemical nature of human or mouse Hb AIc is unknown, both contain a sodium-borohydride-reducible linkage on the beta chain which is a presumed Schiff base between a sugar moiety and the protein. The db/db animals, which have normal amounts of mouse Hb AIc at weaning, show the increase approximately 4 weeks after the onset of the signs of diabetes. This rise is brought about by an increase in a circulating factor that determines directly or indirectly the synthesis of mouse Hb AIc as a post-synthetic modification of Hb A. Evidence for this was obtained by showing that the rate of synthesis of the modified Hb is linear for at least the first 50 days of the life of the red cell and that the rate of synthesis is dependent on the environment in which the cells circulate. Thus the rate of mouse Hb AIc synthesis in +/+ cells is greater when those cells circulate in a db/db host than when they circulate in a +/+ host. The nature of the humoral factor is unknown. If glycosylations of basement membrane proteins and hemoglobin proceed via a common mechanism...
Glycosylated haemoglobins AIa+Ib and AIc were measured serially in 10 consecutive cases of newly discovered non-acidotic diabetes before and after diet and insulin treatment. The average concentration of Hb AIc was 11.4% in untreated diabetics as compared with 4.3% in healthy controls. With prolonged optimal regulation of blood glucose Hb AIc slowly decreased to a mean concentration of 5.5%. The concentration of Hb AIc was significantly correlated with the fasting blood sugar value. The findings suggest that determining Hb AIc may give valuable information on the regulation of carbohydrate metabolism in the preceding one to two months and thus become an important aid to management.
In survival analysis, it is of interest to appropriately select significant predictors. In this paper, we extend the AICC selection procedure of Hurvich and Tsai to survival models to improve the traditional AIC for small sample sizes. A theoretical verification under a special case of the exponential distribution is provided. Simulation studies illustrate that the proposed method substantially outperforms its counterpart: AIC, in small samples, and competes it in moderate and large samples. Two real data sets are also analyzed.
The conventional model selection criterion AIC has been applied to choose candidate models in mixed-effects models by the consideration of marginal likelihood. Vaida and Blanchard (2005) demonstrated that such a marginal AIC and its small sample correction are inappropriate when the research focus is on clusters. Correspondingly, these authors suggested to use conditional AIC. The conditional AIC is derived under the assumptions of the variance-covariance matrix or scaled variance-covariance matrix of random effects being known. We develop a general conditional AIC but without these strong assumptions. This allows Vaida and Blanchard’s conditional AIC to be applied in a wide range. Simulation studies show that the proposed method is promising.
Esta tese aborda aspectos de modelagem e inferência em regressão beta, mais especificamente
melhoramentos do teste de razão da verossimilhanças e proposição e investigação de critérios
de seleção de modelos. O modelo de regressão beta foi proposto por Ferrari e Cribari-Neto
[2004. Beta regression for modeling rates and proportions. J. Appl. Statist. 31, 799 815]
para modelar variáveis contínuas no intervalo (0;1), como taxas e proporções. No primeiro
capítulo, abordamos o problema de inferência em pequenas amostras. Focamos no melhoramento
do teste da razão de verossimilhanças. Consideramos correções de segunda ordem
para a estatística da razão de verossimilhanças em regressão beta em duas abordagens. Determinamos,
por meio de uma abordagem matricial, o fator de correção de Bartlett e também
uma correção de Bartlett Bootstrap. Comparamos os testes baseados nas estatísticas corrigidas
com o teste da razão de verossimilhanças usual e com o teste que utiliza o ajuste de Skovgaard,
que já está proposto na literatura. Os resultados numéricos evidenciam que as correções
de Bartlett são mais acuradas do que a estatística não corrigida e do que o ajuste de Skovgaard.
No segundo e terceiro capítulos...
Il a été démontré que l’hétérotachie, variation du taux de substitutions au cours du temps et entre les sites, est un phénomène fréquent au sein de données réelles. Échouer à modéliser l’hétérotachie peut potentiellement causer des artéfacts phylogénétiques. Actuellement, plusieurs modèles traitent l’hétérotachie : le modèle à mélange des longueurs de branche (MLB) ainsi que diverses formes du modèle covarion. Dans ce projet, notre but est de trouver un modèle qui prenne efficacement en compte les signaux hétérotaches présents dans les données, et ainsi améliorer l’inférence phylogénétique.
Pour parvenir à nos fins, deux études ont été réalisées. Dans la première, nous comparons le modèle MLB avec le modèle covarion et le modèle homogène grâce aux test AIC et BIC, ainsi que par validation croisée. A partir de nos résultats, nous pouvons conclure que le modèle MLB n’est pas nécessaire pour les sites dont les longueurs de branche diffèrent sur l’ensemble de l’arbre, car, dans les données réelles, le signaux hétérotaches qui interfèrent avec l’inférence phylogénétique sont généralement concentrés dans une zone limitée de l’arbre. Dans la seconde étude, nous relaxons l’hypothèse que le modèle covarion est homogène entre les sites...
peer-reviewed; Frailty models are now widely used for analyzing multivariate survival data. An open question is how best to determine how to select the most
appropriate frailty structure supported by the data. Herein, we develop a proce-
dure for selecting the optimal frailty structure from a set of (possibly) non-nested
frailty models. Our focus is on the dispersion parameters which define the frailty
structure. We propose two new AIC criteria: one based on the deviance for goodness of fit and the other on the extended restricted likelihood (ERL) of Lee and
Nelder (1996). A simulation study shows that the AIC based on the extended
restricted likelihood is better when attention is focussed on selecting the frailty
Tanzania has tremendous potential to
support a thriving agribusiness sector. Agriculture is
diverse and extensive, employing more than 80 percent of the
population, and contributing about 28 percent of Gross
Domestic Product, or GDP and 30 percent of export earnings.
A wide range of agricultural commodities are produced in
Tanzania, including fiber (sisal, cotton), beverages
(coffee, tea), sugar, grains (a diverse range of cereals and
legumes), horticulture (temperate and tropical fruits,
vegetables and flowers) and edible oils. This document
proposes a new model for promoting the growth of competitive
value-added sunflower oil processing in Tanzania, and also
seeks to identify potential growth enterprises in other
value chains. The Agribusiness Innovation Center (AIC) will
provide a set of financial and non-financial services to
high-growth potential entrepreneurs, aiming to accelerate
the growth of their enterprises and demonstrating product,
process, and business model innovation across focal sectors.
The AIC will complement existing efforts focused on
farm-level improvements and foreign investment facilitation.
In Santos-Pereira and Pires (Computational Statistics, pp. 291–296. Physica,
Heidelberg, 2002) we proposed a method to detect outliers in multivariate data
based on clustering and robust estimators. To implement this method in practice
it is necessary to choose a clustering method, a pair of location and scatter
estimators, and the number of clusters, k. After several simulation experiments
it was possible to give a number of guidelines regarding the ﬁrst two choices.
However, the choice of the number of clusters depends entirely on the structure
of the particular data set under study. Our suggestion is to try several values
of k (e.g., from 1 to a maximum reasonable k which depends on the number
of observations and on the number of variables) and select k minimizing an
adapted AIC. In this chapter we analyze this AIC-based criterion for choosing
the number of clusters k (and also the clustering method and the location and
scatter estimators) by applying it to several simulated data sets with and without
Senegal has tremendous potential to
raise incomes and create jobs in agriculture. This potential
is particularly strong in the horticulture sector where
Senegal enjoys a comparative advantage because of the
following factors: favorable climatic and water conditions;
capacity to supply European markets at a time when others
cannot; proximity to European markets with availability of
competitive air and sea transport; access to quality inputs;
and few policy distortions. The fact that exports have
increased from 2,700 tons in 1991 to 51,270 tons in 2011
indicates the quality and demand for Senegalese horticulture
products. However, only about 5 percent of the fruits and
vegetables grown in Senegal are processed. The country
imports many processed products that could be produced
competitively domestically, and exports raw materials that,
if processed, could be sold at much higher margins.
Processing could also help reduce post-harvest losses, which
dramatically affect farmers' incomes. The country is,
La AIC es la institución mediante la cual los pueblos indígenas del Cauca participan en el SGSSS. En la práctica se desarrollan una serie de relaciones biopolitícas, afectando la participación de la AIC en el SGSSS porque se generan procesos de subsunción, constituyendo la administración de la vida de las poblaciones por el biopoder; The AIC is the institution through which the indigenous peoples of Cauca participate in the SGSSS. In practice, develop a range of biopolitical relations, affecting the participation of the AIC in the SGSSS because subsumption processes are generated, constituting the administration of the life of the people by biopower
This article reviews the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on latent variable models given their growing use in theory testing and construction. We discuss theoretical statistical results in regression and illustrate more important issues with novel simulations involving latent variable models including factor analysis, latent profile analysis, and factor mixture models. Asymptotically, the BIC is consistent, in that it will select the true model if, among other assumptions, the true model is among the candidate models considered. The AIC is not consistent under these circumstances. When the true model is not in the candidate model set the AIC is effcient, in that it will asymptotically choose whichever model minimizes the mean squared error of prediction/estimation. The BIC is not effcient under these circumstances. Unlike the BIC, the AIC also has a minimax property, in that it can minimize the maximum possible risk in finite sample sizes. In sum, the AIC and BIC have quite different properties that require different assumptions, and applied researchers and methodologists alike will benefit from improved understanding of the asymptotic and finite-sample behavior of these criteria. The ultimate decision to use AIC or BIC depends on many factors...
The Akaike information criterion (AIC) is a widely used tool for model selection. AIC is derived as an asymptotically unbiased estimator of a function used for ranking candidate models which is a variant of the Kullback-Leibler divergence between the true
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc)Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Relevância na Pesquisa
The Akaike information criterion, AIC, and its corrected version, AIC c are two methods for selecting normal linear regression models. Both criteria were designed as estimators of the expected Kullback-Leibler information between the model generating the
The Akaike information criterion (AIC) has been used as a statistical
criterion to compare the appropriateness of different dark energy candidate
models underlying a particular data set. Under suitable conditions, the AIC is
an indirect estimate of the Kullback-Leibler divergence D(T//A) of a candidate
model A with respect to the truth T. Thus, a dark energy model with a smaller
AIC is ranked as a better model, since it has a smaller Kullback-Leibler
discrepancy with T. In this paper, we explore the impact of statistical errors
in estimating the AIC during model comparison. Using a parametric bootstrap
technique, we study the distribution of AIC differences between a set of
candidate models due to different realizations of noise in the data and show
that the shape and spread of this distribution can be quite varied. We also
study the rate of success of the AIC procedure for different values of a
threshold parameter popularly used in the literature. For plausible choices of
true dark energy models, our studies suggest that investigating such
distributions of AIC differences in addition to the threshold is useful in
correctly interpreting comparisons of dark energy models using the AIC
technique.; Comment: Figures and further discussions of the results were added...
AIC is commonly used for model selection but the precise value of AIC has no
direct interpretation. We are interested in quantifying a difference of risks
between two models. This may be useful for both an explanatory point of view or
for prediction, where a simpler model may be preferred if it does nearly as
well as a more complex model. The difference of risks can be interpreted by
linking the risks with relative errors in the computation of probabilities and
looking at the values obtained for simple models. A scale of values going from
negligible to large is proposed. We propose a normalization of a difference of
Akaike criteria for estimating the difference of expected Kullback-Leibler
risks between maximum likelihood estimators of the distribution in two
different models. The variability of this statistic can be estimated. Thus, an
interval can be constructed which contains the true difference of expected
Kullback-Leibler risks with a pre-specified probability. A simulation study
shows that the method works and it is illustrated on two examples. The first is
a study of the relationship between body-mass index and depression in elderly
people. The second is the choice between models of HIV dynamics, where one
model makes the distinction between activated CD4+ T lymphocytes and the other
does not.; Comment: 36 pages
A bias correction to Akaike's information criterion (AIC) is derived for
seemingly unrelated regressions models. The correction is of particular use
when the sample size is not much larger than the number of fitted parameters. A
small-sample simulation study indicates that the bias-corrected AIC (AICc)
provides better model choices than other model selection criteria.; Comment: 9 pages including 1 figure and 3 tables; v2: revtex4, typos corrected