Univariate and Multivariate analysis
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] ...
10
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] ...
24
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 ...
8
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
Value Added Methods: Moving from Univariate to Multivariate Criteria
... Standardized test scores do not capture growth in other school or non-school areas such as self- efficacy, organizational skills, critical thinking, and emotional skills. However, schools target such skills in their ...
14
Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods
... through multivariate modeling yielded a mixture of ...for univariate, bivariate, and trivariate forecast outcomes covering approximately twenty-five years of daily stock market ...series analysis ...
206
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 ...
180
Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
... As mentioned above, alternative block sampling strategies to simulate Λ|R, ω are Shephard and Pitt’s (1997) multi-move sampler and the mixture sampler of Kim et al. (1998). The multi-move sampling procedure divides the ...
24
Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
... As mentioned above, alternative block sampling strategies to simulate Λ|R, ω are Shephard and Pitt’s (1997) multi-move sampler and the mixture sampler of Kim et al. (1998). The multi-move sampling procedure divides the ...
26
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 ...
92
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 ...
8
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 ...
18
The effect of univariate bias adjustment on multivariate hazard estimates
... of univariate bias- adjustment methods for multivariate ...traditional univariate bias-adjustment methods gen- erally reduce biases in impacts that depend on multiple de- pendent ...use ...
13
The effect of univariate bias adjustment on multivariate hazard estimates
... to analysis. The studied time period is 1981–1995. Our analysis is based on daily val- ues during the hottest month of the year at each grid point, which results in about 450 ...our analysis ...
13
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 ...
16
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 ...
240
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 ...
182
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] ...
61
Simple techniques for likelihood analysis of univariate and multivariate stable distributions: with extensions to multivariate stochastic volatility and dynamic factor models
... In univariate stable Paretian distributions, implementation of ABC is conceptually straightforward although certain problems remain open and must be addressed: First, the choice of a proposal distribution for the ...
61
Multivariate models for pretest posttest data and a comparison to univariate models
... 1.2 Univariate methods Due to the popularity of this research design, di↵erent methods have been developed in order to evaluate the data while accounting for ...the analysis of the posttest ...
25