Publications
http://hdl.handle.net/10106/11252
2024-03-28T18:32:36ZA characterization of the Burr Type III and Type XII distributions through the method of percentiles and the Spearman correlation
http://hdl.handle.net/10106/26460
A characterization of the Burr Type III and Type XII distributions through the method of percentiles and the Spearman correlation
Pant, Mohan; Headrick, Todd C.
A characterization of Burr Type III and Type XII distributions based on
the method of percentiles (MOP) is introduced and contrasted with the
method of (conventional) moments (MOM) in the context of estimation
and fitting theoretical and empirical distributions. The methodology is
based on simulating the Burr Type III and Type XII distributions with
specified values of medians, inter-decile ranges, left-right tail-weight
ratios, tail-weight factors, and Spearman correlations. Simulation results
demonstrate that the MOP-based Burr Type III and Type XII distributions
are substantially superior to their (conventional) MOM-based counterparts in terms of relative bias and relative efficiency.
2015-01-01T00:00:00ZUsing a Generalized Linear Mixed Model Approach to Explore the Role of Age, Motor Proficiency, and Cognitive Styles in Children's Reach Estimation Accuracy
http://hdl.handle.net/10106/26459
Using a Generalized Linear Mixed Model Approach to Explore the Role of Age, Motor Proficiency, and Cognitive Styles in Children's Reach Estimation Accuracy
Cacola, Priscila M.; Pant, Mohan
The purpose was to use a multi-level statistical technique to analyze how children's age, motor proficiency, and cognitive styles interact to affect
accuracy on reach estimation tasks via Motor Imagery and Visual Imagery. Results
from the Generalized Linear Mixed Model analysis (GLMM) indicated that only
the 7-year-old age group had significant random intercepts for both tasks. Motor
proficiency predicted accuracy in reach tasks, and cognitive styles (object scale) predicted accuracy in the motor imagery task. GLMM analysis is suitable to explore
age and other parameters of development. In this case, it allowed an assessment of
motor proficiency interacting with age to shape how children represent, plan, and
act on the environment.
2014-01-01T00:00:00ZSimulating Uniform- and Triangular- Based Double Power Method Distributions
http://hdl.handle.net/10106/26353
Simulating Uniform- and Triangular- Based Double Power Method Distributions
Pant, Mohan; Headrick, Todd C.
Power method (PM) polynomials have been used for simulating non-normal
distributions in a variety of settings such as toxicology research, price risk,
business-cycle features, microarray analysis, computer adaptive testing, and
structural equation modeling. A majority of these applications are based on the
method of matching product moments (e.g., skew and kurtosis). However,
estimators of skew and kurtosis can be (a) substantially biased, (b) highly
dispersed, or (c) influenced by outliers. To address this limitation, two families of
double-uniform-PM and double-triangular-PM distributions are characterized
through the method of 𝐿-moments using a doubling technique. The 𝐿-moment
based procedure is contrasted with the method of product moments in the contexts
of fitting real data and estimation of parameters. A methodology for simulating
correlated double-uniform-PM and double-triangular-PM distributions with
specified values of 𝐿-skew, 𝐿-kurtosis, and 𝐿-correlation is also demonstrated.
Monte Carlo simulation results indicate that the L-moment-based estimators of
𝐿-skew, 𝐿-kurtosis, and 𝐿-correlation are superior to their product moment-based
counterparts.
2017-01-01T00:00:00ZCharacterizing Log-Logistic (LL) Distributions through Methods of Percentiles and L-Moments
http://hdl.handle.net/10106/26352
Characterizing Log-Logistic (LL) Distributions through Methods of Percentiles and L-Moments
Pant, Mohan
The main purpose of this paper is to characterize the log-logistic
(LL) distributions through the methods of percentiles and L-moments
and contrast with the method of (product) moments. The method of
(product) moments (MoM) has certain limitations when compared with
method of percentiles (MoP) and method of L-moments (MoLM) in the
context of fitting empirical and theoretical distributions and estimation
of parameters, especially when distributions with greater departure from
normality are involved. Systems of equations based on MoP and MoLM
are derived. A methodology to simulate univariate LL distributions
based on each of the two methods (MoP and MoLM) is developed and
contrasted with MoM in terms of fitting distributions and estimation
of parameters. Monte Carlo simulation results indicate that the MoPand MoLM-based LL distributions are superior to their MoM based
counterparts in the context of fitting distributions and estimation of
parameters.