D Kalman Filtering for Non-Minimal Measurement Models

2005 ◽  
pp. 223-226
Author(s):  
Tine Lefebvre ◽  
Herman Bruyninckx ◽  
Joris De Schutter
Keyword(s):  
Kalman Filtering ◽  
Measurement Models

Approximate Kalman filtering for Bayesian state estimation in nonlinear measurement models

2021 ◽  
Author(s):  
Vijaya Nirmala Gera ◽  
Praveen B. Choppala ◽  
Rajesh P Kumar ◽  
Paul D. Teal
Keyword(s):  
State Estimation ◽  
Kalman Filtering ◽  
Measurement Models

scholarly journals Measurement Models for Time Series Analysis: Estimating Dynamic Linear Errors-in-Variables Models

1998 ◽  
Vol 7 ◽  
pp. 165-186 ◽  
Author(s):  
Gregory E. McAvoy
Keyword(s):  
Measurement Error ◽  
State Space ◽  
Kalman Filtering ◽  
General Description ◽  
Errors In Variables ◽  
Random Measurement Error ◽  
Measurement Models ◽  
Random Measurement ◽  
Space Modeling ◽  
Dynamic Errors

This article uses state space modeling and Kalman filtering to estimate a dynamic linear errors-in-variables model with random measurement error in both the dependent and independent variables. I begin with a general description of the dynamic errors-in-variables model, translate it into state space form, and show how it can be estimated via the Kalman filter. I report the results of a simulation in which the amount of random measurement error is varied, to demonstrate the importance of estimating measurement error models and the superiority that Kalman filtering has over regression. I use the model in a substantive example to examine the effects of public opinion regarding nuclear power on the enforcement decisions of the Nuclear Regulatory Commission. I then estimate a dynamic linear errors-in-variables model using multiple indicators for the latent variables and compare simulations of this model to the single indicator model. Finally, I provide substantive examples which examine the effect of people's economic expectations on their approval of the president and their approval of government more generally.

FACT-2 – The Frankfurt Adaptive Concentration Test

2009 ◽  
Vol 25 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Frank Goldhammer ◽  
Helfried Moosbrugger ◽  
Sabine A. Krawietz
Keyword(s):  
Convergent Validity ◽  
Four Dimensions ◽  
Measurement Models ◽  
Cognitive Failures ◽  
Concentration Test ◽  
Cognitive Failures Questionnaire

The Frankfurt Adaptive Concentration Test (FACT-2) requires discrimination between geometric target and nontarget items as quickly and accurately as possible. Three forms of the FACT-2 were constructed, namely FACT-I, FACT-S, and FACT-SR. The aim of the present study was to investigate the convergent validity of the FACT-SR with self-reported cognitive failures. The FACT-SR and the Cognitive Failures Questionnaire (CFQ) were completed by 191 participants. The measurement models confirmed the concentration performance, concentration accuracy, and concentration homogeneity dimensions of FACT-SR. The four dimensions of the CFQ (i.e., memory, distractibility, blunders, and names) were not confirmed. The results showed moderate convergent validity of concentration performance, concentration accuracy, and concentration homogeneity with two CFQ dimensions, namely memory and distractibility/blunders.

Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽  
2014 ◽  
Vol 10 (4) ◽  
pp. 138-152 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Susan Troncoso Skidmore ◽  
Yan Li ◽  
Bruce Thompson
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Equation Model ◽  
Model Fit ◽  
Model Specification ◽  
Simulation Design ◽  
Measurement Models ◽  
Fit Statistics ◽  
Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

Statistical Inference for Measurement Models

1970 ◽  
Vol 15 (6) ◽  
pp. 402, 404-405
Author(s):  
ROBERT E. DEAR
Keyword(s):  
Statistical Inference ◽  
Measurement Models

Delivering Sustainable Water Systems Using Kalman Filter and Statistical Approach

2017 ◽  
Vol 4 (1) ◽  
pp. 41-52
Author(s):  
Dedy Loebis
Keyword(s):  
Kalman Filtering ◽  
Statistical Approach ◽  
Water Systems ◽  
Data Sets ◽  
Pressure Data ◽  
Burst Event ◽  
Sample Data ◽  
Other Information ◽  
Supply Systems ◽  
Frequency Features

This paper presents the results of work undertaken to develop and test contrasting data analysis approaches for the detection of bursts/leaks and other anomalies within wate r supply systems at district meter area (DMA)level. This was conducted for Yorkshire Water (YW) sample data sets from the Harrogate and Dales (H&D), Yorkshire, United Kingdom water supply network as part of Project NEPTUNE EP/E003192/1 ). A data analysissystem based on Kalman filtering and statistical approach has been developed. The system has been applied to the analysis of flow and pressure data. The system was proved for one dataset case and have shown the ability to detect anomalies in flow and pres sure patterns, by correlating with other information. It will be shown that the Kalman/statistical approach is a promising approach at detecting subtle changes and higher frequency features, it has the potential to identify precursor features and smaller l eaks and hence could be useful for monitoring the development of leaks, prior to a large volume burst event.

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