نتایج جستجو برای: response variable
تعداد نتایج: 1210904 فیلتر نتایج به سال:
Background & objectives: statistical modeling explicates the observed changes in data by means of mathematics equations. In cases that dependent variable is count, Poisson model is applied. If Poisson model is not applicable in a specific situation, it is better to apply the generalized Poisson model. So, our emphasis in this study is to notice the data structure, introducing the generalized Po...
In this paper presents a Maximum Power Point Tracking (MPPT) technique based on the Hill Climbing Search (HCS) method and fuzzy logic system for Wind Turbines (WTs) including of Permanent Magnet Synchronous Generator (PMSG) as generator. In the conventional HCS method the step size is constant, therefor both steady-state response and dynamic response of method cannot provide at the same time an...
A weighted linear regression model with impercise response and p-real explanatory variables is analyzed. The LR fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. A least square solution for estimating the parameters of the model is derived. The result are illustrated by the means of some case studies.
This article focuses on the estimation of population proportion when the study variable is sensitive in nature. Two implicit randomized response techniques are proposed where the unrelated trait can be chosen subjectively. In addition to unbiased estimation of population proportion and variance, an empirical study is conducted to inspect the relative efficiency facet of the proposed techniques....
Introduction: Structural Equation Modeling (SEM) is a very general statistical modeling technique, which is widely used in the behavioral sciences. It can be viewed as a combination of path analysis, regression and factor analysis. One of the prominent features of this method is the ability to compute direct, indirect and total effects, as well as latent variable modeling. Methods: This sy...
The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum’s Three-Parameter models have been implemented, whereas for polytomous data Semejima’s Graded Response model is available. Parameter estimates are obta...
This paper aims to install Latent trait on Association Rule Mining for the semantic analysis of consumer behavior patterns. We adapt Item Response Theory, a famous educational testing model, in order to derive interesting insights from rules by Latent trait. The primary contributions of this paper are fourfold. (1) Latent trait as an unified measure can measure interestingness of derived rules ...
Clarida, Galí and Gertler (CGG 2000), Orphanides and Williams (2005), Kim and Nelson (2006), and others have found time variation in the Fed’s “Taylor Rule” interest rate policy response function. CGG arbitrarily break their period into two fixedcoefficient 20-year subperiods, however, rather than letting the data tell them when and if any shift in the coefficients occurred. They also proxy the...
The high dimensionality of global gene expression profiles, where number of variables (genes) is very large compared to the number of observations (samples), presents challenges that affect generalizability and applicability of microarray analysis. Latent variable modeling offers a promising approach to deal with high-dimensional microarray data. The latent variable model is based on a few late...
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