970 resultados para Bivariate Normal Distribution
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A stochastic metapopulation model accounting for habitat dynamics is presented. This is the stochastic SIS logistic model with the novel aspect that it incorporates varying carrying capacity. We present results of Kurtz and Barbour, that provide deterministic and diffusion approximations for a wide class of stochastic models, in a form that most easily allows their direct application to population models. These results are used to show that a suitably scaled version of the metapopulation model converges, uniformly in probability over finite time intervals, to a deterministic model previously studied in the ecological literature. Additionally, they allow us to establish a bivariate normal approximation to the quasi-stationary distribution of the process. This allows us to consider the effects of habitat dynamics on metapopulation modelling through a comparison with the stochastic SIS logistic model and provides an effective means for modelling metapopulations inhabiting dynamic landscapes.
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The objective is to study beta-amyloid (Abeta) deposition in dementia with Lewy bodies (DLB) with Alzheimer's disease (AD) pathology (DLB/AD). The size frequency distributions of the Abeta deposits were studied and fitted by log-normal and power-law models. Patients were ten clinically and pathologically diagnosed DLB/AD cases. Size distributions had a single peak and were positively skewed and similar to those described in AD and Down's syndrome. Size distributions had smaller means in DLB/AD than in AD. Log-normal and power-law models were fitted to the size distributions of the classic and diffuse deposits, respectively. Size distributions of Abeta deposits were similar in DLB/AD and AD. Size distributions of the diffuse deposits were fitted by a power-law model suggesting that aggregation/disaggregation of Abeta was the predominant factor, whereas the classic deposits were fitted by a log-normal distribution suggesting that surface diffusion was important in the pathogenesis of the classic deposits.
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The size frequency distributions of discrete β-amyloid (Aβ) deposits were studied in single sections of the temporal lobe from patients with Alzheimer's disease. The size distributions were unimodal and positively skewed. In 18/25 (72%) tissues examined, a log normal distribution was a good fit to the data. This suggests that the abundances of deposit sizes are distributed randomly on a log scale about a mean value. Three hypotheses were proposed to account for the data: (1) sectioning in a single plane, (2) growth and disappearance of Aβ deposits, and (3) the origin of Aβ deposits from clusters of neuronal cell bodies. Size distributions obtained by serial reconstruction through the tissue were similar to those observed in single sections, which would not support the first hypothesis. The log normal distribution of Aβ deposit size suggests a model in which the rate of growth of a deposit is proportional to its volume. However, mean deposit size and the ratio of large to small deposits were not positively correlated with patient age or disease duration. The frequency distribution of Aβ deposits which were closely associated with 0, 1, 2, 3, or more neuronal cell bodies deviated significantly from a log normal distribution, which would not support the neuronal origin hypothesis. On the basis of the present data, growth and resolution of Aβ deposits would appear to be the most likely explanation for the log normal size distributions.
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Abnormally enlarged neurons (AEN) occur in many neurodegenerative diseases. To define AEN more objectively, the frequency distribution of the ratio of greatest cell diameter(CD) to greatest nuclear diameter (ND) was studied in populations of cortical neurons in tissue sections of seven cognitively normal brains. The frequency distribution of CD/ND deviated from a normal distribution in 15 out of 18 populations of neurons studied and hence, the 95th percentile (95P) was used to define a limit of the CD/ND ratio excluding the5% most extreme observations. The 95P of the CD/ ND ratio varied from 2.0 to 3.0 in different cases and regions and a value of 95P = 3.0 was chosen to define the limit for normalneurons under non-pathological conditions. Based on the 95P = 3.0 criterion, the proportion of AEN with a CD/ND ≥ 3 varied from 2.6% in Alzheimer's disease (AD) to 20.3% in Pick's disease (PiD). The data suggest: (1) that a CL/ND ≥ 3.0 may be a useful morphological criterion for defining AEN, and (2) AEN were most numerous in PiD and corticobasal degeneration (CBD) and least abundant in AD and in dementia with Lewy bodies (DLB). © 2013 Dustri-Verlag Dr. K. Feistle.
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In many of the Statnotes described in this series, the statistical tests assume the data are a random sample from a normal distribution These Statnotes include most of the familiar statistical tests such as the ‘t’ test, analysis of variance (ANOVA), and Pearson’s correlation coefficient (‘r’). Nevertheless, many variables exhibit a more or less ‘skewed’ distribution. A skewed distribution is asymmetrical and the mean is displaced either to the left (positive skew) or to the right (negative skew). If the mean of the distribution is low, the degree of variation large, and when values can only be positive, a positively skewed distribution is usually the result. Many distributions have potentially a low mean and high variance including that of the abundance of bacterial species on plants, the latent period of an infectious disease, and the sensitivity of certain fungi to fungicides. These positively skewed distributions are often fitted successfully by a variant of the normal distribution called the log-normal distribution. This statnote describes fitting the log-normal distribution with reference to two scenarios: (1) the frequency distribution of bacterial numbers isolated from cloths in a domestic environment and (2), the sizes of lichenised ‘areolae’ growing on the hypothalus of Rhizocarpon geographicum (L.) DC.
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In previous Statnotes, many of the statistical tests described rely on the assumption that the data are a random sample from a normal or Gaussian distribution. These include most of the tests in common usage such as the ‘t’ test ), the various types of analysis of variance (ANOVA), and Pearson’s correlation coefficient (‘r’) . In microbiology research, however, not all variables can be assumed to follow a normal distribution. Yeast populations, for example, are a notable feature of freshwater habitats, representatives of over 100 genera having been recorded . Most common are the ‘red yeasts’ such as Rhodotorula, Rhodosporidium, and Sporobolomyces and ‘black yeasts’ such as Aurobasidium pelculans, together with species of Candida. Despite the abundance of genera and species, the overall density of an individual species in freshwater is likely to be low and hence, samples taken from such a population will contain very low numbers of cells. A rare organism living in an aquatic environment may be distributed more or less at random in a volume of water and therefore, samples taken from such an environment may result in counts which are more likely to be distributed according to the Poisson than the normal distribution. The Poisson distribution was named after the French mathematician Siméon Poisson (1781-1840) and has many applications in biology, especially in describing rare or randomly distributed events, e.g., the number of mutations in a given sequence of DNA after exposure to a fixed amount of radiation or the number of cells infected by a virus given a fixed level of exposure. This Statnote describes how to fit the Poisson distribution to counts of yeast cells in samples taken from a freshwater lake.
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Using a modified deprivation (or poverty) function, in this paper, we theoretically study the changes in poverty with respect to the 'global' mean and variance of the income distribution using Indian survey data. We show that when the income obeys a log-normal distribution, a rising mean income generally indicates a reduction in poverty while an increase in the variance of the income distribution increases poverty. This altruistic view for a developing economy, however, is not tenable anymore once the poverty index is found to follow a pareto distribution. Here although a rising mean income indicates a reduction in poverty, due to the presence of an inflexion point in the poverty function, there is a critical value of the variance below which poverty decreases with increasing variance while beyond this value, poverty undergoes a steep increase followed by a decrease with respect to higher variance. Identifying this inflexion point as the poverty line, we show that the pareto poverty function satisfies all three standard axioms of a poverty index [N.C. Kakwani, Econometrica 43 (1980) 437; A.K. Sen, Econometrica 44 (1976) 219] whereas the log-normal distribution falls short of this requisite. Following these results, we make quantitative predictions to correlate a developing with a developed economy. © 2006 Elsevier B.V. All rights reserved.
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Евелина Илиева Велева - Разпределението на Уишарт се среща в практиката като разпределението на извадъчната ковариационна матрица за наблюдения над многомерно нормално разпределение. Изведени са някои маргинални плътности, получени чрез интегриране на плътността на Уишарт разпределението. Доказани са необходими и достатъчни условия за положителна определеност на една матрица, които дават нужните граници за интегрирането.
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Dependence in the world of uncertainty is a complex concept. However, it exists, is asymmetric, has magnitude and direction, and can be measured. We use some measures of dependence between random events to illustrate how to apply it in the study of dependence between non-numeric bivariate variables and numeric random variables. Graphics show what is the inner dependence structure in the Clayton Archimedean copula and the Bivariate Poisson distribution. We know this approach is valid for studying the local dependence structure for any pair of random variables determined by its empirical or theoretical distribution. And it can be used also to simulate dependent events and dependent r/v/’s, but some restrictions apply. ACM Computing Classification System (1998): G.3, J.2.
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Lognormal distribution has abundant applications in various fields. In literature, most inferences on the two parameters of the lognormal distribution are based on Type-I censored sample data. However, exact measurements are not always attainable especially when the observation is below or above the detection limits, and only the numbers of measurements falling into predetermined intervals can be recorded instead. This is the so-called grouped data. In this paper, we will show the existence and uniqueness of the maximum likelihood estimators of the two parameters of the underlying lognormal distribution with Type-I censored data and grouped data. The proof was first established under the case of normal distribution and extended to the lognormal distribution through invariance property. The results are applied to estimate the median and mean of the lognormal population.
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The aim of this study was to evaluate the degree of conversion (DC) and the cytotoxicity of photo-cured experimental resin composites containing 4-(N,N-dimethylamino)phenethyl alcohol (DMPOH) combined to the camphorquinone (CQ) compared with ethylamine benzoate (EDAB). The resin composites were mechanically blended using 35 wt% of an organic matrix and 65 wt% of filler loading. To this matrix was added 0.2 wt% of CQ and 0.2 wt% of one of the reducing agents tested. 5x1 mm samples (n=5) were previously submitted to DC measurement and then pre-immersed in complete culture medium without 10% (v/v) bovine serum for 1 h or 24 h at 37 °C in a humidifier incubator with 5% CO2 and 95% humidity to evaluate the cytotoxic effects of experimental resin composites using the MTT assay on immortalized human keratinocytes cells. As a result of absence of normal distribution, the statistical analysis was performed using the nonparametric Kruskal-Wallis to evaluate the cytotoxicity and one-way analysis of variance to evaluate the DC. For multiple comparisons, cytotoxicity statistical analyses were submitted to Student-Newman-Keuls and DC analysis to Tukey's HSD post-hoc test (=0.05). No significant differences were found between the DC of DMPOH (49.9%) and EDAB (50.7%). 1 h outcomes showed no significant difference of the cell viability between EDAB (99.26%), DMPOH (94.85%) and the control group (100%). After 24 h no significant difference were found between EDAB (48.44%) and DMPOH (38.06%), but significant difference was found compared with the control group (p>0.05). DMPOH presented similar DC and cytotoxicity compared with EDAB when associated with CQ.
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Universidade Estadual de Campinas . Faculdade de Educação Física
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Prostaglandins control osteoblastic and osteoclastic function under physiological or pathological conditions and are important modulators of the bone healing process. The non-steroidal anti-inflammatory drugs (NSAIDs) inhibit cyclooxygenase (COX) activity and consequently prostaglandins synthesis. Experimental and clinical evidence has indicated a risk for reparative bone formation related to the use of non-selective (COX-1 and COX-2) and COX-2 selective NSAIDs. Ketorolac is a non-selective NSAID which, at low doses, has a preferential COX-1 inhibitory effect and etoricoxib is a new selective COX-2 inhibitor. Although literature data have suggested that ketorolac can interfere negatively with long bone fracture healing, there seems to be no study associating etoricoxib with reparative bone formation. Paracetamol/acetaminophen, one of the first choices for pain control in clinical dentistry, has been considered a weak anti-inflammatory drug, although supposedly capable of inhibiting COX-2 activity in inflammatory sites. OBJECTIVE: The purpose of the present study was to investigate whether paracetamol, ketorolac and etoricoxib can hinder alveolar bone formation, taking the filling of rat extraction socket with newly formed bone as experimental model. MATERIAL AND METHODS: The degree of new bone formation inside the alveolar socket was estimated two weeks after tooth extraction by a differential point-counting method, using an optical microscopy with a digital camera for image capture and histometry software. Differences between groups were analyzed by ANOVA after confirming a normal distribution of sample data. RESULTS AND CONCLUSIONS: Histometric results confirmed that none of the tested drugs had a detrimental effect in the volume fraction of bone trabeculae formed inside the alveolar socket.
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A avaliação do coeficiente de variação (CV) como medida da precisão dos experimentos tem sido feita com diversas culturas, espécies animais e forrageiras por meio de trabalhos sugerindo faixas de classificação dos valores, considerando-se a média, o desvio padrão e a distribuição dos valores de CV das diversas variáveis respostas envolvidas nos experimentos. Neste trabalho, objetivouse estudar a distribuição dos valores de CV de experimentos com a cultura do feijão, propondo faixas que orientem os pesquisadores na avaliação de seus estudos com cada variável. Os dados utilizados foram obtidos de revisão em revistas que publicam artigos científicos com a cultura do feijão. Foram consideradas as variáveis: rendimento, número de vagens por planta, número de grãos por vagem, peso de 100 grãos, estande final, altura de plantas e índice de colheita. Foram obtidas faixas de valores de CV para cada variável tomando como base a distribuição normal, utilizando-se também a distribuição dos quantis amostrais e a mediana e o pseudo-sigma, classificando-os como baixo, médio, alto e muito alto. Os cálculos estatísticos para verificação da normalidade dos dados foram implementados por meio de uma função no software estatístico livre R. Os resultados obtidos indicaram que faixas de valores de CV diferiram entre as diversas variáveis apresentando ampla variação justificando a necessidade de utilizar faixa de avaliação específica para cada variável.
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In this paper, we compare three residuals to assess departures from the error assumptions as well as to detect outlying observations in log-Burr XII regression models with censored observations. These residuals can also be used for the log-logistic regression model, which is a special case of the log-Burr XII regression model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to the modified martingale-type residual in log-Burr XII regression models with censored data.