A volumetric method for building complex models from range images

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.

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Object Detection in Cluttered Range Images Using Edgel Geometry

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.130.1572 ◽

2010 ◽

Vol 130 (9) ◽

pp. 1572-1580

Author(s):

Dipankar Das ◽

Yoshinori Kobayashi ◽

Yoshinori Kuno

Keyword(s):

Object Detection ◽

Range Images

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An Examination of the Effects of Noise Level on Methods to Determine Curvature in Range Images

Electronic Imaging ◽

10.2352/issn.2470-1173.2019.10.iqsp-325 ◽

2019 ◽

Vol 2019 (10) ◽

pp. 325-1-325-7

Author(s):

Jacob D Hauenstein ◽

Timothy S Newman

Keyword(s):

Noise Level ◽

Range Images

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Two-Condition Within-Participant Statistical Mediation Analysis: A Path-Analytic Framework

10.31234/osf.io/4su2j ◽

2019 ◽

Author(s):

Amanda Kay Montoya ◽

Andrew F. Hayes

Keyword(s):

Confidence Intervals ◽

Indirect Effect ◽

Mediation Analysis ◽

Analytic Approach ◽

Analytic Framework ◽

Hypothesis Tests ◽

Bootstrap Confidence Intervals ◽

Complex Models ◽

Path Analytic ◽

Statistical Mediation Analysis

Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of two different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this paper we recast Judd et al.’s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al’s method requires, because it relies only on an inference about the product of paths— the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses.

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