New multiplicities of dreaming and REMing

2000 ◽  
Vol 23 (6) ◽  
pp. 953-955 ◽  
Author(s):  
Harry T. Hunt
Keyword(s):  
Factor Models ◽  
Single Factor ◽  
Multiple Dimensions ◽  
Orientation Response

The five authors vary in the degree to which the recent neuroscience of the REM state leads them towards multiple dimensions and forms of dreaming consciousness (Hobson et al.; Nielsen; Solms) or toward all-explanatory single factor models (Vertes & Eastman, Revonsuo). The view of the REM state as a prolongation of the orientation response to novelty fits best with the former pluralisms but not the latter monisms.[Hobson et al.; Nielsen; Revonsuo; Solms; Vertes & Eastman]

scholarly journals Factor Structure of the “Top Ten” Positive Emotions of Barbara Fredrickson

2021 ◽  
Vol 12 ◽  
Author(s):  
Leopold Helmut Otto Roth ◽  
Anton-Rupert Laireiter
Keyword(s):  
Positive Psychology ◽  
Factor Structure ◽  
Positive Emotions ◽  
Factor Model ◽  
Factor Models ◽  
Cluster Solution ◽  
Special Role ◽  
Single Factor ◽  
Theoretical Significance

In order to contribute to the consolidation in the field of Positive Psychology, we reinvestigated the factor structure of top 10 positive emotions of Barbara Fredrickson. Former research in experimental settings resulted in a three-cluster solution, which we tested with exploratory and confirmatory methodology against different factor models. Within our non-experimental data (N = 312), statistical evidence is presented, advocating for a single factor model of the 10 positive emotions. Different possible reasons for the deviating results are discussed, as well as the theoretical significance to various subfields in Positive Psychology (e.g., therapeutical interventions). Furthermore, the special role of awe within the study and its implications for further research in the field are discussed.

scholarly journals Perceived Stress Scale: confirmatory factor analysis of the PSS14 and PSS10 versions in two samples of pregnant women from the BRISA cohort

2017 ◽  
Vol 33 (12) ◽  
Author(s):  
Ana Valéria Carvalho Pires Yokokura ◽  
Antônio Augusto Moura da Silva ◽  
Juliana de Kássia Braga Fernandes ◽  
Cristina Marta Del-Ben ◽  
Felipe Pinheiro de Figueiredo ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Pregnant Women ◽  
Perceived Stress ◽  
Discriminant Validity ◽  
Factor Models ◽  
Single Factor ◽  
Perceived Stress Scale ◽  
Stress Scale ◽  
Confirmatory Factor

This study aimed to assess the dimensional structure, reliability, convergent validity, discriminant validity, and scalability of the Perceived Stress Scale (PSS). The sample consisted of 1,447 pregnant women in São Luís (Maranhão State) and 1,400 in Ribeirão Preto (São Paulo State), Brazil. The 14 and 10-item versions of the scale were assessed using confirmatory factor analysis, using weighted least squares means and variance (WLSMV). In both cities, the two-factor models (positive factors, measuring resilience to stressful situations, and negative factors, measuring stressful situations) showed better fit than the single-factor models. The two-factor models for the complete (PSS14) and reduced scale (PSS10) showed good internal consistency (Cronbach’s alpha ≥ 0.70). All the factor loadings were ≥ 0.50, except for items 8 and 12 of the negative dimension and item 13 of the positive dimension. The correlations between both dimensions of stress and psychological violence showed the expected magnitude (0.46-0.59), providing evidence of an adequate convergent construct validity. The correlations between the scales’ positive and negative dimensions were around 0.74-0.78, less than 0.85, which suggests adequate discriminant validity. Extracted mean variance and scalability were slightly higher for PSS10 than for PSS14. The results were consistent in both cities. In conclusion, the single-factor solution is not recommended for assessing stress in pregnant women. The reduced, 10-item two-factor scale appears to be more appropriate for measuring perceived stress in pregnant women.

The dependability of the general factor of intelligence: Why small, single-factor models do not adequately represent g

Intelligence ◽  
2011 ◽  
Vol 39 (5) ◽  
pp. 418-433 ◽  
Author(s):  
Jason T. Major ◽  
Wendy Johnson ◽  
Thomas J. Bouchard
Keyword(s):  
Factor Models ◽  
General Factor ◽  
Single Factor

Interest rate volatility and the shape of the term structure

1994 ◽  
Vol 347 (1684) ◽  
pp. 563-576 ◽  
Keyword(s):  
Interest Rate ◽  
Term Structure ◽  
Yield Curve ◽  
Factor Models ◽  
The State ◽  
Rate Curve ◽  
Forward Rate ◽  
State Variables ◽  
Single Factor ◽  
Interest Rate Volatility

This paper analyses the effect of interest rate uncertainty on the shape of the forward rate curve. We consider a broad class of term structure models characterized by an affine relation between the drift and diffusion coefficients of the stochastic process describing the evolution of the state variables and the level of the state variables. For these models, a simple relation exists between the shape of the forward rate curve, the sensitivity of the zero-coupon yield curve to the state variables and the variance-covariance matrix of the state variables. In single factor models this relation implies that minus the convexity of the forward rate curve with respect to a measure of ‘duration’ is equal to the variance of the short rate. The paper explores why it is that, despite the well known shortcomings of single factor models, attempts to fit such models to cross-sections of nominal bond prices nonetheless produce reasonable estimates of interest rate volatility.

scholarly journals Inversion of Chlorophyll-a Concentration in Donghu Lake Based on Machine Learning Algorithm

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1179
Author(s):  
Xiaodong Tang ◽  
Mutao Huang
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Learning Algorithm ◽  
Factor Models ◽  
Machine Learning Algorithm ◽  
Single Factor ◽  
Chlorophyll A Concentration ◽  
Donghu Lake ◽  
Svm Model ◽  
Better Than

Machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. In this work, a variety of models were built by applying five kinds of datasets and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The results revealed that modeling with multi-factor datasets has the possibility to improve the accuracy of inversion model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. The SVM model based on seven three-band combination dataset (SVM3) is the best inversion one among all multi-factor models that the mean relative error (MRE), mean absolute error (MAE), root mean square error (RMSE) of the SVM model based on single-factor dataset (SF-SVM) are 30.82%, 9.44 μg/L and 12.66 μg/L, respectively. SF-SVM performs best in single-factor models, MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69 μg/L and 16.49 μg/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.

scholarly journals Application of GA-SVM method with parameter optimization for landslide development prediction

2013 ◽  
Vol 1 (5) ◽  
pp. 5295-5322 ◽  
Author(s):  
X. Z. Li ◽  
J. M. Kong
Keyword(s):  
Parameter Optimization ◽  
Large Scale ◽  
Factor Models ◽  
Support Vector ◽  
Single Factor ◽  
Svm Model ◽  
High Prediction ◽  
Basic Characteristics ◽  
Landslide Development ◽  
The Right

Abstract. Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering area of Southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that, the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest RSME of 0.0009 and the biggest RI of 0.9992.

scholarly journals A Bi-Factor Approach to Modeling the Structure of Worry and Rumination

2016 ◽  
Vol 8 (3) ◽  
pp. 252-264 ◽  
Author(s):  
Juyoen Hur ◽  
Wendy Heller ◽  
Justin L. Kern ◽  
Howard Berenbaum
Keyword(s):  
Factor Model ◽  
Factor Models ◽  
Depression Symptoms ◽  
Potential Utility ◽  
Single Factor ◽  
Common Variance ◽  
Specific Factors ◽  
Anxiety Depression ◽  
The Relationship ◽  
Functional Process

It remains unclear whether worry and rumination represent the same functional process, or if they are unique constructs. The current study examined the relationship between worry and rumination, focusing on the potential utility of a bi-factor approach as an alternative to “common” vs. “distinctive” approaches. The results indicated that the structural relationship between worry and rumination is best represented by a bi-factor model (compared to single-factor and two-factor models), which is comprised of a single factor that captures common variance in worry and rumination, as well as separate worry-specific and rumination-specific factors that capture unique variance. Furthermore, three orthogonal factors derived from the bi-factor model showed diverging associations with motivational traits (avoidance and approach temperament) and distinct anxiety/depression symptoms. The bi-factor conceptualization provides a framework for reconciling the diverging perspectives regarding worry and rumination, suggesting the need to pay attention to both common and unique aspects of worry and rumination.

When Factors Do Not Span Their Basis Portfolios

2018 ◽  
Vol 53 (6) ◽  
pp. 2335-2354 ◽  
Author(s):  
Mark Grinblatt ◽  
Konark Saxena
Keyword(s):  
Factor Models ◽  
Optimal Portfolio ◽  
Single Factor

To price assets with a parsimonious set of factor-mimicking portfolios, one typically identifies and weights well-diversified basis portfolios. Traditional weightings lead to factor-mimicking portfolios that are unlikely to price even the basis portfolios from which they are formed. We offer a method to combine basis portfolios into a single factor-mimicking portfolio that is closely linked to the optimal portfolio. In practice, this method improves the pricing accuracy of parsimonious factor models, even for anomaly portfolios formed from characteristics that are distinct from those underlying the basis portfolios.

scholarly journals The Diabetes Eating Problem Survey-Revised (DEPS-R) in a Greek Adult Population with Type 1 Diabetes Mellitus: Model Comparison Supporting a Single Factor Structure

Nutrients ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 2375
Author(s):  
Calliope Karastogiannidou ◽  
Parthena Giannoulaki ◽  
Ioannis Samaras ◽  
Evangelia Kotzakioulafi ◽  
Triantafyllos Didangelos ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Disordered Eating ◽  
Factor Structure ◽  
Model Comparison ◽  
Factor Models ◽  
Single Factor ◽  
Eating Problem

Type 1 diabetes mellitus (T1DM) patients occasionally develop disordered eating behaviors, leading to insulin manipulation without medical consultation, targeting to achieve weight control. In clinical practice, the Diabetes Eating Problem Survey-Revised Version (DEPS-R) questionnaire has been used to evaluate eating disorders in T1DM patients. This study was conducted to validate the factor structure of the Greek version of DEPS-R using Confirmatory Factor Analysis (CFA), to investigate its reliability and convergent validity in Greek T1DM adults and to compare a single factor DEPS-R model with multiple factor models. Participants were 103 T1DM adults receiving insulin, who responded to DEPS-R. Their anthropometric, biochemical and clinical history data were evaluated. The sample presented good glycemic control and 30.1% scored above the established DEPS-R cut-off score for disturbed eating behavior. CFA results revealed that the data fit well to the factor models. The DEPS-R scale had good reliability and was positively linked to BMI, HbA1c, total daily dose and time in range. Model comparison supported the superiority of the 1-factor model, implying that Greek clinicians and practitioners might not have to consider individualized treatment based on various scores across different subscales but they can adopt a single DEPS-R score for an easy and efficient screening for disordered eating.

Sign in / Sign up

Export Citation Format

Share Document