Parametric statistics

About: Parametric statistics is a research topic. Over the lifetime, 39200 publications have been published within this topic receiving 765761 citations.

Parametric and Non Parametric Approach in Structural Equation Modeling (SEM): The Application of Bootstrapping

Abstract: Lately, there was some attention for the Variance Based SEM (VB-SEM) against that of Covariance Based SEM (CB-SEM) from social science researches regarding the fitness indexes, sample size requirement, and normality assumption Not many of them aware that VB-SEM is developed based on the non-parametric approach compared to the parametric approach of CB-SEM In fact the fitness of a model should not be taken lightly since it reflects the behavior of data in relation to the proposed model for the study Furthermore, the adequacy of sample size and the normality of data are among the main assumptions of parametric test itself This study intended to clarify the ambiguities among the social science community by employing the data-set which do not meet the fitness requirements and normality assumptions to execute both CB-SEM and VB-SEM The findings reveal that the result of CB-SEM with bootstrapping is almost similar to that of VB-SEM (bootstrapping as usual) Therefore, the failure to meet the fitness and normality requirements should not be the reason for employing Non-Parametric SEM

mu analysis with real parametric uncertainty

Abstract: The authors give a broad overview, from a LFT (linear fractional transformation)/ mu perspective, of some of the theoretical and practical issues associated with robustness in the presence of real parametric uncertainty, with a focus on computation. Recent results on the properties of mu in the mixed case are reviewed, including issues of NP completeness, continuity, computation of bounds, the equivalence of mu and its bounds, and some direct comparisons with Kharitonov-type analysis methods. In addition, some advances in the computational aspects of the problem, including a branch-and-bound algorithm, are briefly presented together with the mixed mu problem may have inherently combinatoric worst-case behavior, practical algorithms with modes computational requirements can be developed for problems of medium size ( >

Bayesian Framework for Characterizing Geotechnical Model Uncertainty

Abstract: As any model is only an abstraction of the real world, model uncertainty always exists. The magnitude of model uncertainty is important for geotechnical decision making. If model uncertainty is not considered, the geotechnical predictions and hence the decisions based on the geotechnical predictions might be biased. In this study, a framework for characterizing geotechnical model uncertainty using observation data is proposed. The framework is based on the concept of multivariable Bayesian updating, in which the statistics of model uncertainty are updated using observed performance data. Uncertainties in both input parameters and observed data can be considered in the proposed framework. To bypass complex computational works involved in the proposed framework, a practical approximate solution is presented. The proposed framework is illustrated by characterizing the model uncertainty of four limit equilibrium methods for slope stability analysis using quality centrifuge test data. Parametric study in the illustrative example shows that both quality and quantity of the performance data could affect the determination of the model uncertainty, and that such effects can be systematically quantified with the proposed method.

Parametric estimation of technical and allocative inefficiency with panel data.

Abstract: The error-components approach to estimating allocative inefficiency imposes restrictive assumptions on the distributions of the errors and functional form. The parametric approach does not require special assumptions about the error distribution or technology but typically assumes technical efficiency or restrictive functional forms. The parametric approach also allows for systematic firm responses to shadow prices. The authors generalize the parametric approach to a panel data setting and show that input and firm-specific allocative inefficiency, as well as firm-specific technical inefficiency, can be identified and estimated using a flexible functional form. This is demonstrated empirically with an application to U.S. airlines. Copyright 1994 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Identification of Parametric Variations of Structures Based on Least Squares Estimation and Adaptive Tracking Technique

Abstract: An important objective of health monitoring systems for civil infrastructures is to identify the state of the structure and to detect the damage when it occurs. System identification and damage detection, based on measured vibration data, have received considerable attention recently. Frequently, the damage of a structure may be reflected by a change of some parameters in structural elements, such as a degradation of the stiffness. Hence it is important to develop data analysis techniques that are capable of detecting the parametric changes of structural elements during a severe event, such as the earthquake. In this paper, we propose a new adaptive tracking technique, based on the least-squares estimation approach, to identify the time-varying structural parameters. In particular, the new technique proposed is capable of tracking the abrupt changes of system parameters from which the event and the severity of the structural damage may be detected. The proposed technique is applied to linear structures, including the Phase I ASCE structural health monitoring benchmark building, and a nonlinear elastic structure to demonstrate its performance and advantages. Simulation results demonstrate that the proposed technique is capable of tracking the parametric change of structures due to damages.