Sentences with phrase «of nonparametric»

Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed poison model

Overview of nonparametric and multivariate analysis.

Not exact matches

We compared socio - demographic and pregnancy - related characteristics among the three study groups using chi - square tests for categorical variables, analysis of variance (ANOVA) for normally distributed continuous variables and the nonparametric Kruskal - Wallis test for continuous variables that were not normally distributed.

Hopkins, D.J., King, G. (2010) A method of automated nonparametric content analysis for social science, American Journal of Political Science, 54 (1), 229 - 247.

To avoid potential problems of non-normality and sensitivity to outliers, we chose this nonparametric approach over the standard Pearson correlation coefficient (46).

Quality of life was significantly higher in participants randomized to the intervention group, who demonstrated a statistically and clinically meaningful increase in QOL at 12 weeks (P <.05 using the nonparametric Wilcoxon test).31, 32 Median survival was almost 5 months longer in the intervention group.

Analysis of baseline data using an ANOVA for parametric data and a Kruskal — Wallis test for nonparametric data showed that all groups were equivalent on all sleep measures (i.e., time needed to fall asleep, number and length of night - time awakenings, total time asleep, fatigue level next day).

In addition to the question of the use and impact of more generic teaching styles, we were able to apply nonparametric analyses to two additional reading - specific teaching domains — word recognition and comprehension instruction.

These scans, or snapshots, become data points that could then be classified into categories of instructional practice and analyzed using descriptive and nonparametric statistics.

The Effect of Private Tutoring Expenditures on Academic Performance: Evidence from a Nonparametric Bounding Method

Due to the nonnormality of the distribution, we compared results from both parametric and nonparametric tests.

Specific statistical areas of expertise include factor and cluster analysis, basic bivariate analyses, repeated measures analyses, linear and hierarchical / mixed models, structural equation modeling, and nonparametric analyses including logistic regression techniques.

«Nonparametric Estimation and Testing of Stochastic Discount Factor.»

A multilevel decomposition of school performance using robust nonparametric frontier techniques

For me, the most important part of the study is the finding that «The nonparametric monotonicity relation test indicates that the differences in the total return of the equal - weighted portfolio and the value - and price - weighted portfolios is monotonically related to size, price, liquidity and idiosyncratic volatility.»

Nonparametric estimates of posterior marginal densities were computed with Gibanal (van Kaam, 1998).

Estimated nonparametric marginal densities for polygenic (σ2u) and major gene (σ2w) variance for elbow dysplasia (ED) in the Golden Retriever, as an example of all breeds.

Estimated nonparametric marginal densities for polygenic (σ2u) and major gene (σ2w) variance for hip dysplasia (HD) in the German Shepherd, as an example of all breeds.

It uses a non-parametric trend estimate, but points out that the results are insensitive to the use of paramateric or nonparametric methods.

Malikov, Emir and Sun, Kai and Kumbhakar, Subal C. (2018): Nonparametric Estimates of the Clean and Dirty Energy Substitutability.

In the appendix, they describe (without using this phrase) a nonparametric bootstrap to estimate standard errors, but the method used is inappropriate in the presence of serial correlation.

Nonparametric bootstrapping was used to obtain the bias - corrected and accelerated confidence intervals of the indirect effect (28).

Development and validation of a brief version of the Dyadic Adjustment Scale with a nonparametric item analysis model.

Bootstrapping is a nonparametric approach to statistical inference that does not make a priori assumptions about a sampling distribution (e.g., does not necessitate a normal distribution of scores for a given variable), and empirically derives its sampling distribution from the study's data [61].