General Measurement Model Research Articles

Unified Observability Analysis and Measurement Placement in Generalized State Estimation

This paper deals with the problem of observability analysis and measurement placement in generalized state estimation, where substations are modeled at the physical-level (bus-section/switching-device model) by explicit representation of circuit breakers. The conventional state estimator is extended by incorporating the active and reactive power flows through circuit breakers as state variables. The generalized measurement model is transformed to a conventional node frame of reference formulation. Any circuit breaker (flow measured or not) is substituted by two ficticious nodes and branches, the two pairs of the corresponding voltage angles and magnitudes, and additional pseudo measurements. A very efficient numerical method is proposed for observability testing and identification of observable islands. When the network is unobservable multiple measurement placement is accomplished in a noniterative manner. Results from a test system are presented.

Acoustic vector-sensor processing in the presence of a reflecting boundary

We consider the passive direction-of-arrival (DOA) estimation problem using arrays of acoustic vector sensors located in a fluid at or near a reflecting boundary. We formulate a general measurement model applicable to any planar surface, derive an expression for the Cramer-Rao bound (CRB) on the azimuth and elevation of a single source, and obtain a bound on the mean-square angular error (MSAE). We then examine two applications of great practical interest: hull-mounted and seabed arrays. For the former, we use three models for the hull: an ideal rigid surface for high frequency, an ideal pressure-release surface for low frequency, and a more complex, realistic layered model. For the seabed scenario, we model the ocean floor as an absorptive liquid layer. For each application, we use the CRB, MSAE bound, and beam patterns to quantify the advantages of using velocity and/or vector sensors instead of pressure sensors. For the hull-mounted application, we show that normal component velocity sensors overcome the well-known, low-frequency problem of small pressure signals without the need for an undesirable stand-off distance. For the seabed scenario, we also derive a fast wideband estimator of the source location using a single vector sensor.

Categorical Causal Modeling

Latent class analysis (LCA) is an extremely useful and flexible technique for the analysis of categorical data, measured at the nominal, ordinal, or interval level (the latter with fixed or estimated scores). It is, first, a general measurement model, a particular kind of latent structure model that can be used for the investigation of the reliability and validity of categorical measurements, taking both random and systematic response errors into account. When dealing with test-retest effects, response consistency effects, unobserved heterogeneity, response effects from varying survey (interviewing) conditions, and so on, it is useful to take a consistent causal modeling point of view and to integrate LCA into a general causal log-linear model with latent variables. The resulting directed log-linear modeling approach integrates insights from Goodman's modified path approach, the modified LISREL approach, and directed graphical modeling.

Confirmatory P-technique analyses of therapist discourse: high-versus low-quality child therapy sessions.

From a sample of 35 child therapy sessions, 3 of the highest and 3 of the lowest quality sessions (HQS and LQS) were identified by independent ratings on the Loyola Child Psychotherapy Process Scales. Each of the 535 therapist-utterances in the HQS and the 372 therapist-utterances in the LQS were rated on 15 language interaction scales. Principal-components P-technique analyses were applied separately to the HQS and LQS, revealing 3 factors, Responsive Informing (RI), Initiatory Questioning (IQ), and Positive Regard (PR), that seemed to correspond across session types. Using confirmatory P-technique, a general measurement model with acceptable goodness-of-fit was determined for HQS. Two factors emerged: RI and IQ but not PR. Using a bootstrap procedure, random halves of the HQS were repeatedly sampled and compared via multigroup confirmatory factor analysis to create a reference distribution of the magnitudes of their difference chi-squares. Random resamples of HQS and LQS were then compared, resulting in a second reference distribution. Statistical tests and effect-size measures revealed replicable and large differences between sessions types.

A distributed bearings-only tracking algorithm using reduced sufficient statistics

A distributed parameter estimation algorithm is presented for a general nonlinear measurement model with additive Gaussian noise. We show that the Bayes-closed estimation algorithm developed by Kulhavy, when extended to the multisensor case leads to a linear fusion rule, regardless of the form of the local a posteriori densities. Specifically, the Kulhavy algorithm generates a set of reduced sufficient statistics (RSS) representing the local sensor densities, which are simply added and subtracted at the global processor to obtain optimum fusion. We discuss various approximations to the Bayes-closed algorithm which lead to a practical parameter estimator for the nonlinear measurement model, and apply such an approximate technique to the bearings-only tracking problem. The performance of the distributed tracker is compared with an alternative algorithm based on the extended Kalman filter (EKF) implemented in modified polar coordinates (MPC). It is shown that the Bayes-closed estimator does not diverge in the sense of an ordinary EKF, and hence the Bayes-closed technique can be employed in both a unidirectional and bidirectional transmission mode.

A double-hurdle labour supply model with fallible indicators of labour force state

We modify the formulation of a labour supply model with unemployment, to allow for measurement errors in some of the indicators used to operationally identify the labour force state of each individual. The resulting model consists of the combination of two models: (I) the model of interest is a reduced form bivariate Probit model, aimed at explaining jointly the participation/non-participation decision and the employment/ unemployment outcome; (II) to this model, we superimpose a fairly general and flexible measurement model, specifying how discrete indicators - obtained from responses to a relevant set of questionnaire items - measure the labour force states. We establish conditions for model identification, and present a sensible iterative strategy for obtaining ML estimates. Evidence from a sample of married women in the Italian Labour Force Survey corroborates the intuitively plausible notion that parameter estimates are highly sensitive to alternative ways of defining unemployment, and documents the advantages of the proposed model.

Measurement Models for Electrochemical Impedance Spectroscopy: I . Demonstration of Applicability

Use of measurement models is suggested as an intermediate step in the analysis of impedance data. In a manner analogous to the routine use of measurement models in the deconvolution of optical spectra, the measurement model could be used to guide development of physicoelectrochemical models by determining whether a data set is consistent with the Kramers‐Kronig relations, by suggesting a form for the error structure of the data, and by providing an indication of the number and type of physical processes that can be resolved from the data. In this paper, a general measurement model is shown to apply for a wide variety of typical electrochemical impedance spectra. The application of the measurement models as a data filter will be addressed in subsequent papers.

Paradigmatic Distinctions between Instructionally Relevant Measurement Models

In this article, we delineate essential commonalities and distinctions between two approaches to measurement for instructional decision making. Specific subskill mastery measurement is explained using a case study, and salient features of this predominant model are described. Then, a major contrasting approach, the general outcome measurement model, is explained; a curriculum-based measurement case study is provided to illustrate general outcome measurement; and the essential features of this alternative model are reviewed. Finally, we describe how general outcome measurement represents an innovative approach to assessment by bridging traditional and contemporary paradigms.

Measuring Interstate Affect

This article advances a general formula that can be applied to classified behavioral data to yield a theoretically informed summary measure of interstate affect. Although this measurement procedure is constructed with exclusive reference to expressed affect-that is, a government's feelings of friendliness or hostility toward some external object or actorit is easily adapted to other properties embracing positive, neutral, and negative categories. Once the general measurement model is obtained, certain modifications are introduced that take account of varying time order effects in the development of affective attitudes and the occasional need for monadic-level measurement. The study concludes with an empirical example in which the affect measure is applied to World Event Interaction Survey (WEIS)encoded events exchanged between the United States and the Soviet Union. An actionreaction model fit to bilateral, quarterly affect scores revealed that these two nations follow virtually identical processes in generating affective evaluations of one another.