Univariate and multivariate analysis of clinicopathological variables
Advantages of multivariate analysis of the perception of crowding on the Tongariro Crossing compared with univariate analysis
... Using univariate analysis to investigate the perception of crowding on Tongariro Crossing we would be interested in the occurrence of crowding, for example, the percentage of the part[r] ...
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Evaluation of genetic diversity among soybean (Glycine max) genotypes using univariate and multivariate analysis.
... Twenty-four soybean genotypes were evaluated, and of these, 17 were lineages proceeding from the Soybean Breeding Program of Universidade Federal de Uberlândia, and 7 were commercial c[r] ...
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Comparison of univariate and multivariate linkage analysis of traits related to hypertension
... the multivariate analysis of TG, LDL, and HDL would be more powerful for detecting HTN genes via linkage analysis compared with univariate analysis of ...linkage analysis of four ...
5
Assessing Univariate and Multivariate Normality, A Guide For Non-Statisticians
... both univariate and multivariate analysis (Rosner, ...t-tests, Analysis of Variance (ANOVA), just to name a few, are based on the normal distribution (Neter et ...many multivariate ...
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Multivariate analysis of clinicopathological and prognostic significance of miRNA 106b~25 cluster in gastric cancer
... In prognostic analyze, we used Kaplan–Meier analy- sis to prognostic univariate analysis. The Cox regres- sion (or proportional hazards regression) is a statistical approach for investigating the effect of ...
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Statistical Models and Analysis of Univariate and Multivariate Degradation Data
... For degradation data in reliability analysis, estimation of the first-passage time (FPT) distribution to a threshold provides valuable information on reliability characteristics. Re- cently, Balakrishnan and Qin ...
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Univariate and multivariate Pareto models
... socio-economic variables. Univariate and multivariate variations ...Pareto variables in terms of independent gamma distributed ...used multivariate Pareto models share the same ...
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Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
... to multivariate Gaussian densities which are used as proposal densities to draw volatility blocks within an AR-MH ...auxiliary variables S which indicate from which of the normal distributions a particular ...
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Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
... to multivariate Gaussian densities which are used as proposal densities to draw volatility blocks within an AR-MH ...auxiliary variables S which indicate from which of the normal distributions a particular ...
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Bias in Estimating Multivariate and Univariate Diffusions
... among variables and allow for multiple determining factors, many continuous systems are speci…ed in multivariate ...of multivariate continuous time models in macroeconomics; Sundaresan (2000) ...
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Univariate and multivariate pattern analysis of preterm subjects: a multimodal neuroimaging study
... using univariate and multivariate pattern ...the univariate model of the amplitude of low-frequency BOLD fluctuations (ALFF) and gray matter volume (GMV) making use of resting-state fMRI and ...
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Predictive factors for infertility of women: an univariate and multivariate logistic regression analysis
... present study, the univariate and multivariate logistic regression showed that the husband’s education could be considered as a predictor of female infertility. In most cases, higher income levels have been ...
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Univariate and Multivariate Time Series Manifold Learning
... series analysis aims to extract meaningful information from data that has been generated in sequence by a dynamic ...component analysis where fewer components are retained in order to identify the ...
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Shapelet Transforms for Univariate and Multivariate Time Series Classification
... In support of this thesis a new distance early abandon method is proposed. A class balancing algorithm is implemented, which uses a one vs. all multi class information gain that enables heuristics which were developed ...
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On the Univariate Representation of Multivariate Volatility Models with Common Factors
... where ^ i is the i-th smallest eigenvalue of the estimated matrix (17). The test statistic (18) follows asymptotically a 2 (v) distribution under the null where = snq s(n s) or = sN q s(N s). The MLE of the matrix is ...
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Essays on univariate and multivariate modeling of financial market risks
... While there are plenty of studies on the characteristics of financial time-series data in the econometrics literature, modeling internet search queries has not yet been suf- ficiently investigated. At the model ...
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The effect of univariate bias adjustment on multivariate hazard estimates
... two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of ...
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Univariate and Multivariate GARCH Models Applied to the CARBS Indices
... ferent univariate and multivariate GARCH family ...empirical analysis, the symmet- ric GARCH, and asymmetric GJR-GARCH and EGARCH models were estimated for the CARBS indices and a global minimum ...
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The effect of univariate bias adjustment on multivariate hazard estimates
... climate variables can be evaluated against observed quantities in the climate do- main, the implications for modeled impacts are much harder to ...climate variables across time and space (Zscheischler et ...
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Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models
... In the case of general (non-symmetric) multivariate stable distributions the samples. , so estimates , 0 can be obtained along with and.[r] ...
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