83,019 research outputs found
Generalized Network Psychometrics: Combining Network and Latent Variable Models
We introduce the network model as a formal psychometric model,
conceptualizing the covariance between psychometric indicators as resulting
from pairwise interactions between observable variables in a network structure.
This contrasts with standard psychometric models, in which the covariance
between test items arises from the influence of one or more common latent
variables. Here, we present two generalizations of the network model that
encompass latent variable structures, establishing network modeling as parts of
the more general framework of Structural Equation Modeling (SEM). In the first
generalization, we model the covariance structure of latent variables as a
network. We term this framework Latent Network Modeling (LNM) and show that,
with LNM, a unique structure of conditional independence relationships between
latent variables can be obtained in an explorative manner. In the second
generalization, the residual variance-covariance structure of indicators is
modeled as a network. We term this generalization Residual Network Modeling
(RNM) and show that, within this framework, identifiable models can be obtained
in which local independence is structurally violated. These generalizations
allow for a general modeling framework that can be used to fit, and compare,
SEM models, network models, and the RNM and LNM generalizations. This
methodology has been implemented in the free-to-use software package lvnet,
which contains confirmatory model testing as well as two exploratory search
algorithms: stepwise search algorithms for low-dimensional datasets and
penalized maximum likelihood estimation for larger datasets. We show in
simulation studies that these search algorithms performs adequately in
identifying the structure of the relevant residual or latent networks. We
further demonstrate the utility of these generalizations in an empirical
example on a personality inventory dataset.Comment: Published in Psychometrik
CHOICE OF FLEXIBLE FUNCTIONAL FORMS: REVIEW AND APPRAISAL
Choice between alternative flexible functional forms has received little explicit treatment in many empirical agricultural studies. Theoretical criteria and empirical techniques for choosing between flexible functional forms are reviewed. Theoretical topics include definitions of flexibility, mathematical expansions, separability, and regular regions. Empirical techniques examined are Monte Carlo analysis, parametric modeling, bayesian inference, and nonnested hypothesis testing. Comparison of the full range of theoretical and empirical aspects may provide more credible and reliable empirical estimates when consumer or producer duality assumptions are appropriate in agricultural applications.Research Methods/ Statistical Methods,
Consistent tests of conditional moment restrictions
We propose two classes of consistent tests in parametric econometric models defined through multiple conditional moment restrictions. The first type of tests relies on nonparametric estimation, while the second relies on a functional of a marked empirical process. For both tests, a simulation procedure for obtaining critical values is shown to be asymptotically valid. Finite sample performances of the tests are investigated by means of several Monte-Carlo experiments.Publicad
Testing a Parametric Function Against a Nonparametric Alternative in IV and GMM Settings
This paper develops a specification test for functional form for models identified by moment restrictions, including IV and GMM settings. The general framework is one where the moment restrictions are specified as functions of data, a finite-dimensional parameter vector, and a nonparametric real function (an infinite-dimensional parameter vector). The null hypothesis is that the real function is parametric. The test is relatively easy to implement and its asymptotic distribution is known. The test performs well in simulation experiments.Generalized method of moments, specification test, nonparametric alternative, LM statistic, generalized arc-sine distribution
A semiparametric regression model for paired longitudinal outcomes with application in childhood blood pressure development
This research examines the simultaneous influences of height and weight on
longitudinally measured systolic and diastolic blood pressure in children.
Previous studies have shown that both height and weight are positively
associated with blood pressure. In children, however, the concurrent increases
of height and weight have made it all but impossible to discern the effect of
height from that of weight. To better understand these influences, we propose
to examine the joint effect of height and weight on blood pressure. Bivariate
thin plate spline surfaces are used to accommodate the potentially nonlinear
effects as well as the interaction between height and weight. Moreover, we
consider a joint model for paired blood pressure measures, that is, systolic
and diastolic blood pressure, to account for the underlying correlation between
the two measures within the same individual. The bivariate spline surfaces are
allowed to vary across different groups of interest. We have developed related
model fitting and inference procedures. The proposed method is used to analyze
data from a real clinical investigation.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS567 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
- …