scholarly journals Sparse Solutions to Complex Models

2013 ◽  
pp. 37-59
Author(s):  
Xin Chen ◽  
Chung Piaw Teo
Keyword(s):  
Complex Models ◽  
Sparse Solutions

Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

2019 ◽  
Vol 35 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Dorota Reis
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Interoceptive Awareness ◽  
Multidimensional Assessment ◽  
New Instrument ◽  
Confirmatory Factor ◽  
Complex Models ◽  
Approximate Zero ◽  
Bayesian Structural Equation Modeling

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.

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

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.

scholarly journals Kinetic mixing, dark photons and extra dimensions. Part III. Brane localized dark matter

2021 ◽  
Vol 2021 (3) ◽  
Author(s):  
Thomas G. Rizzo ◽  
George N. Wojcik
Keyword(s):  
Dark Matter ◽  
Extra Dimensions ◽  
Model Building ◽  
New Physics ◽  
Model Complexity ◽  
Complex Scalar ◽  
Dark Photon ◽  
Complex Models ◽  
The Cost ◽  
D Model

Abstract Extra dimensions have proven to be a very useful tool in constructing new physics models. In earlier work, we began investigating toy models for the 5-D analog of the kinetic mixing/vector portal scenario where the interactions of dark matter, taken to be, e.g., a complex scalar, with the brane-localized fields of the Standard Model (SM) are mediated by a massive U(1)D dark photon living in the bulk. These models were shown to have many novel features differentiating them from their 4-D analogs and which, in several cases, avoided some well-known 4-D model building constraints. However, these gains were obtained at the cost of the introduction of a fair amount of model complexity, e.g., dark matter Kaluza-Klein excitations. In the present paper, we consider an alternative setup wherein the dark matter and the dark Higgs, responsible for U(1)D breaking, are both localized to the ‘dark’ brane at the opposite end of the 5-D interval from where the SM fields are located with only the dark photon now being a 5-D field. The phenomenology of such a setup is explored for both flat and warped extra dimensions and compared to the previous more complex models.

scholarly journals Parametric bootstrap estimators for hybrid inference in forest inventories

2017 ◽  
Vol 91 (3) ◽  
pp. 354-365 ◽  
Author(s):  
Mathieu Fortin ◽  
Rubén Manso ◽  
Robert Schneider
Keyword(s):  
Multiple Imputation ◽  
Nonlinear Models ◽  
Parametric Bootstrap ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Resampling Methods ◽  
Forest Inventories ◽  
Point Estimator ◽  
Complex Models ◽  
High Level

Abstract In forestry, the variable of interest is not always directly available from forest inventories. Consequently, practitioners have to rely on models to obtain predictions of this variable of interest. This context leads to hybrid inference, which is based on both the probability design and the model. Unfortunately, the current analytical hybrid estimators for the variance of the point estimator are mainly based on linear or nonlinear models and their use is limited when the model reaches a high level of complexity. An alternative consists of using a variance estimator based on resampling methods (Rubin, D. B. (1987). Multiple imputation for nonresponse surveys. John Wiley & Sons, Hoboken, New Jersey, USA). However, it turns out that a parametric bootstrap (BS) estimator of the variance can be biased in contexts of hybrid inference. In this study, we designed and tested a corrected BS estimator for the variance of the point estimator, which can easily be implemented as long as all of the stochastic components of the model can be properly simulated. Like previous estimators, this corrected variance estimator also makes it possible to distinguish the contribution of the sampling and the model to the variance of the point estimator. The results of three simulation studies of increasing complexity showed no evidence of bias for this corrected variance estimator, which clearly outperformed the BS variance estimator used in previous studies. Since the implementation of this corrected variance estimator is not much more complicated, we recommend its use in contexts of hybrid inference based on complex models.

scholarly journals Near-Surface Mounted Reinforcement of Sawn Timber Beams-FEM Approach

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2780
Author(s):  
Izabela Burawska-Kupniewska ◽  
Piotr Beer
Keyword(s):  
European Standard ◽  
Near Surface ◽  
Bending Tests ◽  
Starting Point ◽  
Structural Applications ◽  
Bending Stresses ◽  
Complex Models ◽  
Near Surface Mounted ◽  
Sawn Timber ◽  
Timber Beams

The demand for timber has increased significantly in recent years. Therefore, reliable tools are needed to predict the mechanical properties of sawn timber, especially for structural applications. Very complex models require a lot of input data for analysis, which cannot always be guaranteed, especially in industrial practice. Thus, a simplified model for material description was developed and assessed with experiments (static bending tests carried out in accordance with the guidelines suggested in the European standard EN 408) and an analytical approach (gamma method according to the guidelines given in the European standard EN 1995). The effective stiffness was calculated as a major parameter, which has an influence on the elements’ behavior. The model included a near-surface mounted (NSM) local reinforcement technique, with CFRP strips of Scots pine timber beams being subjected to bending stresses. It is anticipated that the developed model can be a starting point for the repair engineering field, contributing to decision-making regarding conservation technique selection and range. Next, improvements of the model will provide more and more realistic results for numerical analysis in terms of the obtained failure mechanisms for sawn timber elements.

scholarly journals Advanced Spheroid, Tumouroid and 3D Bioprinted In-Vitro Models of Adult and Paediatric Glioblastoma

2021 ◽  
Vol 22 (6) ◽  
pp. 2962
Author(s):  
Louise Orcheston-Findlay ◽  
Samuel Bax ◽  
Robert Utama ◽  
Martin Engel ◽  
Dinisha Govender ◽  
...  
Keyword(s):  
Life Expectancy ◽  
High Throughput Screening ◽  
High Grade Glioma ◽  
Tumour Microenvironment ◽  
In Vitro Models ◽  
Treatment Modalities ◽  
High Grade ◽  
Future Improvement ◽  
Complex Models

The life expectancy of patients with high-grade glioma (HGG) has not improved in decades. One of the crucial tools to enable future improvement is advanced models that faithfully recapitulate the tumour microenvironment; they can be used for high-throughput screening that in future may enable accurate personalised drug screens. Currently, advanced models are crucial for identifying and understanding potential new targets, assessing new chemotherapeutic compounds or other treatment modalities. Recently, various methodologies have come into use that have allowed the validation of complex models—namely, spheroids, tumouroids, hydrogel-embedded cultures (matrix-supported) and advanced bioengineered cultures assembled with bioprinting and microfluidics. This review is designed to present the state of advanced models of HGG, whilst focusing as much as is possible on the paediatric form of the disease. The reality remains, however, that paediatric HGG (pHGG) models are years behind those of adult HGG. Our goal is to bring this to light in the hope that pGBM models can be improved upon.

scholarly journals How to train your differentiable filter

2021 ◽  
Author(s):  
Alina Kloss ◽  
Georg Martius ◽  
Jeannette Bohg
Keyword(s):  
Bayesian Filtering ◽  
Learning Approaches ◽  
Filtering Algorithms ◽  
Recursive Filtering ◽  
Process Dynamics ◽  
Noise Characteristics ◽  
Complex Models ◽  
End To End ◽  
Robotic Applications ◽  
And Training

AbstractIn many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution. Bayesian Filtering algorithms address this state estimation problem, but they require models of process dynamics and sensory observations and the respective noise characteristics of these models. Recently, multiple works have demonstrated that these models can be learned by end-to-end training through differentiable versions of recursive filtering algorithms. In this work, we investigate the advantages of differentiable filters (DFs) over both unstructured learning approaches and manually-tuned filtering algorithms, and provide practical guidance to researchers interested in applying such differentiable filters. For this, we implement DFs with four different underlying filtering algorithms and compare them in extensive experiments. Specifically, we (i) evaluate different implementation choices and training approaches, (ii) investigate how well complex models of uncertainty can be learned in DFs, (iii) evaluate the effect of end-to-end training through DFs and (iv) compare the DFs among each other and to unstructured LSTM models.

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