Residual Covariance Research Articles

Factor Structure of the PANAS With Bayesian Structural Equation Modeling in a Chinese Sample.

The Positive and Negative Affect Schedule (PANAS) is the most widely used self-report instrument for assessing affect. However, there are inconsistent findings regarding the factor structure of the PANAS. In this study, we applied Bayesian structural equation modeling (BSEM) to investigate the structure of the PANAS using data from a sample of 893 Chinese middle and high school students. Four models, the orthogonal two-, the oblique two-, the three-, and the bi-factor models were tested with prior specifications including approximately zero cross-loadings and residual covariances. The results indicated that the orthogonal two-factor model specified with informative priors for both cross-loadings and residual correlations has the best model fit. Confirmatory factor analysis with the maximum likelihood estimator (ML-CFA) based on modifications from BSEM analysis showed improved model fit compared to ML-CFA based on frequentist analysis, which is the evidence for the merit of BSEM for addressing misspecifications.

Design of borehole deployments for slope stability analysis based on a probabilistic approach

This study proposes a cross-correlation map-based borehole deployment approach for two-dimensional probabilistic slope stability analysis. This approach designs the layout of the proper number of boreholes based on the cross-correlation between the factor of safety and spatially variable soil strength every part of a slope. Numerically synthesized, undrained slopes are investigated as examples to illustrate the effectiveness of the proposed approach. Results demonstrate that the proposed approach is viable, and the cross-correlation maps are the appropriate metric for design slope borehole deployment. Using the cross-correlation maps, a small number of boreholes can sufficiently capture the large-scale heterogeneities that are critical to the slope stability. This information can help to identify the slip surface and improve the slope stability analysis. The small-scale heterogeneity, due to its short correlation structure or the residual covariance of the soil property field after conditioning using the borehole data, leads to a small amount of uncertainty in slope stability analysis. This small uncertainty could be vital to the slope stability analysis when the slope stability is close to the limit equilibrium state.

Psychometric properties of the Chinese version of Five Facet Mindfulness Questionnaire\u2014short form in cancer patients: a Bayesian structural equation modeling approach

BackgroundMindfulness has emerged as an important correlate of well-being in various clinical populations. The present study evaluated the psychometric properties of the 20-item short form of the Five Facet Mindfulness Questionnaire (FFMQ-SF) in the Chinese context.MethodsThe study sample was 127 Chinese colorectal cancer patients who completed the FFMQ-SF and validated physical and mental health measures. Factorial validity of the FFMQ-SF was assessed using Bayesian structural equation modeling (BSEM) via informative priors on cross-loadings and residual covariances. Linear regression analysis examined its convergent validity with the health measures on imputed datasets.ResultsThe five-factor BSEM model with approximate zero cross-loadings and one residual covariance provided an adequate model fit (PPP = 0.07, RMSEA = 0.06, CFI = 0.95). Satisfactory reliability (ω = 0.77–0.85) was found in four of the five facets (except nonjudging). Acting with awareness predicted lower levels of perceived stress, negative affect, anxiety, depression, and illness symptoms (β = − 0.37 to − 0.42) and better quality of life (β = 0.29–0.32). Observing, nonjudging, and nonreacting did not show any significant associations (p > .05) with health measures. Acting with awareness was not significantly correlated (r < 0.15) with the other four facets.ConclusionThe present findings provide partial support for the psychometric properties of the FFMQ-SF in colorectal cancer patients. The nonjudging facet showed questionable validity and reliability in the present sample. Further studies with larger sample sizes are needed to elucidate the viability of FFMQ-SF as a measure of mindfulness facets in cancer patients.

Bayesian Approach to Multivariate Component-Based Logistic Regression: Analyzing Correlated Multivariate Ordinal Data

Applications of component-based models have gained much attention as a means of accompanying dimension reduction in the regression setting and have been successfully implemented to model a univariate outcome in the behavioral and social sciences. Despite the prevalence of correlated ordinal outcome data in the fields, however, most of the extant component-based models have been extended to address the multivariate ordinal issue with a simplified but unrealistic assumption of independence, which may lead to biased statistical inferences. Thus, we propose a Bayesian methodology for a component-based model that accounts for unstructured residual covariances, while regressing multivariate ordinal outcomes on pre-defined sets of predictors. The proposed Bayesian multivariate ordinal logistic model re-expresses ordinal outcomes of interest with a set of latent continuous variables based on an approximate multivariate t-distribution. This contributes not only to developing an efficient Gibbs sampler, a Markov Chain Monte Carlo algorithm, but also to facilitating the interpretation of regression coefficients as log-transformed odds ratio. The empirical utility of the proposed method is demonstrated through analyzing a subset of data, extracted from the 2009 to 2010 Health Behavior in School-Aged Children study that investigates risk factors of four different forms of bullying perpetration and victimization: physical, social, racial, and cyber.

A Bayesian Structural Equation Modeling Factor Analysis of the UCLA Posttraumatic Stress Disorder Reaction Index for DSM-5 in a Polyvictimized Sample of Adolescents.

The revised criteria for posttraumatic stress disorder (PTSD) in the fifth edition of the Diagnostic and Statistical Manual necessitated the development of new screening tools for youth, one of the most widely used of which is the UCLA Posttraumatic Stress Disorder Reaction Index for DSM-5 (RI-5). Thus far, the few studies that have investigated the RI-5's factor structure have supported a four-factor model. However, to date this research has been limited to youth with histories of exposure to single-event traumatic stressors, a significant limitation as evidence suggests many trauma-exposed youth report exposure to multiple types of traumatic stressors, or polyvictimization. It is imperative to determine the generalizability of previous factor models to specific populations which they are purported to represent. We investigated whether the RI-5's four-factor model replicated in a sample of 455 polyvictimized justice-involved adolescents. Initial confirmatory factor analysis demonstrated that the four-factor model did not converge. Therefore, we utilized Bayesian Structural Equations Modeling (BSEM) to determine why the previously proposed factor structure did not converge. The BSEM model suggested that the global factor structure was acceptable and did not require addition or subtraction of any factor or cross-factor loadings. However, small and moderate residual covariances resulted in model misspecification, suggesting there may be additional associations not captured by the current DSM-5 model for polyvictimized youth. Future work should continue examining the RI-5's factor structure in order to better understand whether the current results are unique and how measurements assessing DSM-5 PTSD symptom criteria perform in diverse trauma-exposed youth populations.

Examining the factor structure of the Big Five Inventory-2 personality domains with an adolescent sample.

We examined the within-domain structure of the five domains of personality measured by the Big Five Inventory-2 with data collected from an adolescent sample (N = 838). Three possible factor models were tested: a single factor, correlated facets, and a single factor with correlated residuals. We examined each model controlling for acquiescence, a response bias in which respondents tend to agree/disagree regardless of item content, using two approaches: acquiescence factor and within-person centering of item-level responses. Across each domain, results indicated both the correlated facets and correlated residuals models demonstrated acceptable fit. Accounting for acquiescent responding was generally associated with improved model fit. However, consistent with past struggles in measuring open-mindedness in adolescents, the correlated residuals model with acquiescence as a factor for open-mindedness failed to converge. Regularized structural equation modeling was conducted on this model for open-mindedness and suggested certain residual covariances that contributed to estimation difficulties should be constrained to zero. Advantages of models are discussed with implications for studying the Big Five personality domains in adolescents. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Full-Duplex Amplify-and-Forward MIMO Relaying: Design and Performance Analysis Under Erroneous CSI and Hardware Impairments

Full-duplex amplify-and-forward multiple-input multiple-output relaying has been the focus of several recent studies, due to the potential for achieving a higher spectral efficiency and lower latency, together with inherent processing simplicity. However, when the impact of hardware distortions are considered, such relays suffer from a distortion-amplification loop , due to the inter-dependent nature of the relay transmit signal covariance and the residual self-interference covariance. The aforementioned behavior leads to a significant performance degradation for a system with a low or medium hardware accuracy. In this work, we analyze the relay transfer function as well as the mean squared-error performance of a full-duplex amplify-and-forward multiple-input multiple-output relaying communication, under the impact of collective sources of additive and multiplicative transmit and receive impairments. Building on the performed analysis, an optimization problem is devised to minimize the communication Mean Squared-Error and solved by employing the recently proposed penalty dual-decomposition framework. The proposed solution converges to a stationary point of the original problem via a sequence of convex quadratic programs, thereby enjoying an acceptable arithmetic complexity as the problem dimensions grow large. Numerical simulations verify the significance of the proposed distortion-aware design and analysis, compared to the common simplified approaches, as the hardware accuracy degrades.

Clinical Learning Evaluation Questionnaire: A Confirmatory Factor Analysis.

IntroductionThe Clinical Learning Evaluation Questionnaire (CLEQ) is a multidimensional, reliable instrument designed to measure the effectiveness of the clinical learning environment for undergraduate medical students. This study seeks to measure and examine the underlying construct along with the latent variables by conducting a confirmatory factor analysis, using structural equation modeling (SEM) so that the instrument can be utilized as an evaluation tool for the continuous improvement of educational environments and curricula.MethodsA cross-sectional study was carried out on 185 third- and fourth-year medical students. A confirmatory factor analysis was conducted, beginning with principal component analysis for standardized factor loadings, using varimax rotation in SPSS to explore the underlying construct of items. The constructs to which each item was tied were determined, and then the data were run through AMOS to assess construct validity through item reduction based on the modification indices, and estimates were made of the standardized residual covariance of each item in order to determine the best model fit.ResultsA total of 185 students completed the CLEQ Inventory. The original six-factor structure of the CLEQ did not achieve model fit (X2=1587.475, RMSEA=0.092, RMR=0.146, GFI=0.651, AGFI=0.601, CFI=0.728, NFI=0.626). However, the suggested four-factor model of CLEQ displayed good model fit with the improvement of values (X2=86.184, RMSEA=0.052, RMR=0.062, GFI=0.903, AGFI=0.865, CFI=0.951, NFI=0.871). Internal consistency analysis showed that Cronbach’s alpha values of the original six-factor model ranged from 0.68 to 0.88, while four-factor model ranged from 0.72 to 0.87.ConclusionThis study did not support the proposed six-factor structure of the CLEQ tool. However, the four-factor CLEQ structure demonstrated an adequate degree of good fit and was found to be as reliable as the original structure. Further research on the predictive validity of CLEQ is required as well as a comparison of the psychometric properties across different institutions and countries.

Inter-protein residue covariation information unravels physically interacting protein dimers

BackgroundPredicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown interacting counterparts. Most of co-evolutionary methods discover a combination of physical interplays and functional associations. However, there are only a handful of approaches which specifically infer physical interactions. Hybrid co-evolutionary methods exploit inter-protein residue coevolution to unravel specific physical interacting proteins. In this study, we introduce a hybrid co-evolutionary-based approach to predict physical interplays between pairs of protein families, starting from protein sequences only.ResultsIn the present analysis, pairs of multiple sequence alignments are constructed for each dimer and the covariation between residues in those pairs are calculated by CCMpred (Contacts from Correlated Mutations predicted) and three mutual information based approaches for ten accessible surface area threshold groups. Then, whole residue couplings between proteins of each dimer are unified into a single Frobenius norm value. Norms of residue contact matrices of all dimers in different accessible surface area thresholds are fed into support vector machine as single or multiple feature models. The results of training the classifiers by single features show no apparent different accuracies in distinct methods for different accessible surface area thresholds. Nevertheless, mutual information product and context likelihood of relatedness procedures may roughly have an overall higher and lower performances than other two methods for different accessible surface area cut-offs, respectively. The results also demonstrate that training support vector machine with multiple norm features for several accessible surface area thresholds leads to a considerable improvement of prediction performance. In this context, CCMpred roughly achieves an overall better performance than mutual information based approaches. The best accuracy, sensitivity, specificity, precision and negative predictive value for that method are 0.98, 1, 0.962, 0.96, and 0.962, respectively.ConclusionsIn this paper, by feeding norm values of protein dimers into support vector machines in different accessible surface area thresholds, we demonstrate that even small number of proteins in pairs of multiple alignments could allow one to accurately discriminate between positive and negative dimers.

Inheritance of leaf resistance to Sclerotinia sclerotiorum in Brassica napus and its genetic correlation with cotyledon resistance

Research to control yield losses from Sclerotinia (Sclerotinia sclerotiorum) in oilseed rape (Brassica napus) has focused on stem resistance. However, resistance to leaf infection against this pathogen would also be beneficial, both in limiting additional plant leaf damage and in reducing inoculum build up within-crop and resultant spread onto stems. Three B. napus breeding populations, C2 (NC-8 × RQ-001-NCA-8 NC2-7), C5 (cv. Charlton × RQ-001-NCA-8 NC2-7) and C6 (cv. Charlton × NC4-5), were screened for leaf resistance (based on mean lesion diameter) under controlled environment conditions. Each population consisted of parents (P1 and P2), F1, F2, BC1P1 and BC2P2, except for C5 that lacked BC1P1. Moderate broad sense heritability for leaf resistance (0.45) to S. sclerotiorum was only found in population C6, where genetic variance was mostly non-additive. Analyses of generation means and variances indicated that both dominance and complex epistatic interactions were present in C6. Bivariate analysis revealed a positive genetic covariance between the non-additive effects for mean leaf lesion and cotyledon lesion diameters, and significant negative covariance of residuals, which supports a common genetic control of cotyledon and leaf resistance to S. sclerotiorum. These results will guide breeders in selection and development of genotypes with both cotyledon and leaf resistance against this important pathogen worldwide.

Inventář tendence k vině a hanbě (ITVAH): pilotní studie

This paper constitutes a pilot study of a novel measure for assessing proneness to guilt and proneness to shame, Inventář tendence k vině a hanbě (ITVAH, Inventory of Proneness to Guilt and Shame). ITVAH is a scenario-based method distinguishing affective and behavioural components of guilt and shame and also considering the private or public experience of guilt and shame, respectively. Two items (affective and behavioural reaction) were created for each of the 16 scenarios, resulting in total of 32 items. A four-factor measurement model was assumed – Negative behaviour evaluation (NBE), Reparation, Negative self-evaluation (NSE), Withdrawal. Next, we assumed negative correlations of NSE and Withdrawal with self-esteem and self-forgiveness and positive correlations of NBE and Reparation with self-forgiveness. The pilot study was carried out on 249 Czech-speaking adults (67 % women, aged 18–75, Md = 22). An ordinal confirmatory factor analysis with residual covariances between items connected to the same scenarios was conducted. The hypothesized four-factor model was found acceptable. However, we found limited evidence for the hypothesized relationships with self-esteem and self-forgiveness – in contrast to theoretical expectations we found a medium-strong negative relationship between self-forgiveness and affective component of guilt. For further use of the measure in the Czech and Slovak environment it is necessary to assess additional evidence of construct validity and concurrent validity with The Test of Self-Conscious Affect-3 (TOSCA-3).

Discrepant Inferences When Modeling Associations Between Time-Varying Exposures and Linear Growth Trajectories in Infancy Using Length-For-Age Z Scores Versus Raw Length

Abstract Objectives To compare inferences from longitudinal models of the relationships between biomarkers of interest and linear growth outcomes in infancy using length-for-age z-scores (LAZ) based on age- and sex-specific growth standards, or raw length. Methods This was a secondary analysis of data from a study of the association between bone-related biomarkers and infant linear growth trajectories up to 1 year of life in a subset of infants (n = 820) enrolled in the Maternal Vitamin D for Infant Growth trial. The linear growth outcome (LAZ or raw length) was modelled as a function of the interaction between each biomarker and age using linear mixed effect models with restricted cubic splines. Models were specified to obtain the best fit and reconcile discrepancies in results from LAZ and length models. Inferences from marginal effects at birth, 3 months, 6 months, and 12 months were compared, for a total 4 effect estimates from each of 10 pairs of LAZ and length models, resulting in 40 pairs of estimates. The following biomarkers were included: fibroblast growth factor 21 (FGF21), fibroblast growth factor 23 (FGF23), N-terminal propeptide of C-type natriuretic peptide (NT-proCNP), osteocalcin, osteoprotegerin, receptor activator of nuclear activator kappa-b ligand (RANKL), 25-hydroxyvitamin D (25OHD), C-reactive protein (CRP), Interleukin 6 (IL6), and insulin-like growth factor-1 (IGF1). Biomarkers were time-varying, measured in cord blood and at 3 and 6 months of age. Results The best fitting model for LAZ had 3 knots with random slopes, and the best fitting model for raw length had 5 knots, random slopes, and an exponential residual covariance structure. Comparisons of the pairs of marginal estimates from the LAZ vs length models resulted in discrepant inferences for 25% of effect estimates (10/40). Results were consistently concordant only for FGF23, 25OHD, CRP, and IL6. Conclusions Length and LAZ represent the same biological construct but their use in longitudinal modelling may lead to different inferences about associations between time-varying exposures and infant growth, possibly due to residual confounding by age. These findings raise concerns about the reliability of studies of determinants or markers of infant linear growth based on longitudinal modelling of growth trajectories. Funding Sources Bill and Melinda Gates Foundation.

Factorial Structure and Invariance Analysis of the BIS–BAS Scale, IPIP-R Version

Gray’s Reinforcement Sensitivity Theory is a neurobiological model developed to explain the regulation of behavior and emotions, using the BIS and BAS systems as motivational constructs based on variables of sensitivity to reinforcement, associated with the emergence of internalizing and externalizing psychopathologies. The objective was to analyze the factor structure, and invariance of the BIS–BAS IPIP-R translated into the Spanish language. A non-probabilistic sampling of 288 people between the ages of 18 and 45 from the city of Bogota, Colombia (167 women and 121 men, average age of 28.92 (SD = 7.53) was carried out. Among the results, a two-dimensional structure was verified with correlated factors and residual covariances, once included when reviewing the modification rates and the favorable adjustment of the model. In the invariance analysis of the correlated two-dimensional model, when imposing progressive and sequential restrictions, differential values are presented, indicating that the factor loads are invariant in nested groups. In conclusion, evidence of the two correlated dimensions that support Gray’s theory of activation and inhibition systems independent of sex and age was obtained.

Structural facets of POU2F1 in light of the functional annotations and sequence-structure patterns

POU domain class 2 homebox 1 or POU2F1 is broadly known as an important transcription factor. Due to its association with different types of malignancies, POU2F1 became one of the key factors in pancancer analysis. However, in spite of considering this protein as a potential drug target, none of the drug targeting POU2F1 has been designed as of yet due to the extreme structural flexibility of this protein. In this article, we have proposed a three-level comprehensive framework for understanding the structural conservation and co-variation of POU2F1. First, a gene regulatory network based on the normal and pathological functions of POU2F1 has been created for better understanding the strong association between POU2F1 deregulation and cancers. After that, based on the evolutionary sequence space analysis, the comparative sequence dynamics of the protein members of POU domain family has been studied mostly between non-human and human species. Subsequently, the reciprocity effect of the residual co-variation has been identified through direct coupling analysis. Along with that, the structure of POU2F1 has been analyzed depending on quality assessment and normal mode-based structure network. Comparing the sequence and structure space information, the most significant set of residues viz., 3, 9, 13, 17, 20, 21, 28, 35, and 36 have been identified as structural facet for function. This study demonstrates that the structural malleability of POU2F1 serves as one of the prime reason behind its functional multiplicity in terms of protein moonlighting. Communicated by Ramaswamy H. Sarma

(Structural) VAR Models with Ignored Changes in Mean and Volatility

We discuss estimation of so-called long vector autoregressions for multivariate series exhibiting possibly time-varying mean and (co)variances. In applied work, such changes often escape undetected, and we ask how standard tools (least squares estimation, point forecasts, and estimated impulse responses) are affected when ignoring the changes altogether. Keeping the order of the autoregression fixed is known to lead to asymptotic bias in autoregressive parameter estimators in the presence of ignored changes in the mean. Yet we show that allowing the complexity of the model to increase with the sample size leads to consistent estimators of the AR coefficient matrices individually. The fitted long VAR models appear to have unit root behavior, in spite of the absence of any stochastic trend in the model, and may even mimic cointegration; but, in spite of the structural change in the data generating process, out-of-sample forecasts based on long VARs are consistent. These findings hold under constant as well as under time-varying covariances. In what concerns estimated impulse responses, their sampling behavior depends primarily on whether the residual covariance matrix is employed for identification of the structural shocks or not. While MA coefficient matrices obtained by inversion of the fitted long VAR model are consistent for the true coefficients under mild additional restrictions even under time-varying error (co)variances, the residual covariance matrix estimator converges to an average covariance matrix, such that localized estimators may be more suitable for a precise identification. Monte Carlo simulations and empirical illustration support our theoretical findings. Empirical relevance of the theory is illustrated through two illustrations: (i) international dynamics of inflation and (ii) uncertainty and economics activity.

Investigation of multivariate seismic surrogate demand modeling for multi-response structural systems

Probabilistic seismic risk assessment of complex multi-response structural systems requires accurate estimations of multiple engineering demand parameters (EDPs) that are related to different failure modes. One conventional way is developing multiple separate univariate seismic demand models for each of the EDPs. However, such an approach lacks sufficient consideration of the correlations among different EDPs and also requires repetitive model tuning and training. Another alternative, which has not yet been extensively studied, is to develop a single multivariate surrogate demand model (MvSDM) that is able to jointly provide the estimates for all the EDPs. To facilitate more accurate and realistic seismic demand modeling of multi-response structural systems, the present study conducts a comprehensive investigation of different MvSDMs, which are constituted by a systematic trend model that characterizes the mean response hypersurface, and a covariance model that quantifies the correlated model errors. Three types of systematic trend models including multivariate linear regression (MvLR), partial least squares regression (PLSR) and artificial neural network (ANN); along with three model error covariance estimation methods including joint probabilistic demand model (JPSDM) method, sample regression residual covariance (SCOV) method and multi-output Gaussian process regression (MOGP), are considered. The influence of trend model and error covariance model selection on seismic demand and fragility modeling is then studied based on a typical three-span concrete-girder highway bridge structure in the Central and Southeastern US. The results suggest that kernel PLSR and ANN as the systematic trend models, and the SCOV method as the error covariance model are promising alternatives that can contribute to accurate and reliable multivariate surrogate demand modeling yet with good computational efficiency.

Single nucleotide polymorphism heritability and differential patterns of genetic overlap between inattention and four neurocognitive factors in youth.

Theoretical models of attention-deficit/hyperactivity disorder implicate neurocognitive dysfunction, yet neurocognitive functioning covers a range of abilities that may not all be linked with inattention. This study (a) investigated the single nucleotide polymorphism (SNP) heritability (h2SNP) of inattention and aspects of neurocognitive efficiency (memory, social cognition, executive function, and complex cognition) based on additive genome-wide effects; (b) examined if there were shared genetic effects among inattention and each aspect of neurocognitive efficiency; and (c) conducted an exploratory genome-wide association study to identify genetic regions associated with inattention. The sample included 3,563 participants of the Philadelphia Neurodevelopmental Cohort, a general population sample aged 8-21 years who completed the Penn Neurocognitive Battery. Data on inattention was obtained with the Kiddie Schedule of Affective Disorders (adapted). Genomic relatedness matrix restricted maximum likelihood was implemented in genome-wide complex trait analysis. Analyses revealed significant h2SNP for inattention (20%, SE = 0.08), social cognition (13%, SE = 0.08), memory (17%, SE = 0.08), executive function (25%, SE = 0.08), and complex cognition (24%, SE = 0.08). There was a positive genetic correlation (0.67, SE = 0.37) and a negative residual covariance (-0.23, SE = 0.06) between inattention and social cognition. No SNPs reached genome-wide significance for inattention. Results suggest specificity in genetic overlap among inattention and different aspects of neurocognitive efficiency.

Factorial Analysis and Invariance Testing for Age and Gender of the Reactive-Proactive Aggression Questionnaire (RPQ).

Reactive aggression is characterized by high emotional activation, impulsivity, and hostility, while proactive aggression presents a cold, instrumental, and planned strategy. The aim was to perform a psychometric analysis of the Reactive-Proactive Aggression Questionnaire [RPQ]. A non-probability sample of 502 people between 18 and 40 years old was formed, grouped by sex (n=297, 59.2% women and n=205, 40.8% men) and age (n = 224, 44.62% under 25 years old and n=278, 55.38% over 25 years old). The instruments were the RPQ, the Anger Rumination Scale (ARS), and an affective scale (PANAS) in printed format. In this instrumental psychometric study, we found that the RPQ fits better in a model of two factors interrelated with residual covariances (CFI = .928, RMSEA = .044), presenting significant correlations with negative affect and anger rumination, as evidence of validity of concurrent criterion, especially with reactive aggression (anger rumination r = .542, and negative affect r = .359). Also, the test was not invariant between sexes and ages, given that the best fit was in the male sex and those under 25 years of age (∆CFI < 0.01, ∆RMSEA < 0.015). We concluded that women and adults over the age of 25 have a different aggressive response profile. These findings represent new directions of research around the measurement of aggressive behavior and the development of gender differentiated interventions for adolescents and young adults.

Target Detection With Imperfect Waveform Separation in Distributed MIMO Radar

This paper considers target detection in distributed multiple-input multiple-output (MIMO) radar with imperfect waveform separation at local receivers. The problem is formulated as a binary composite hypothesis testing problem, where target residuals due to imperfect waveform separation are explicitly modeled as a subspace component in the alternative hypothesis, while disturbances including the clutter and thermal noise are present under both hypotheses. Under assumptions of fluctuating and non-fluctuating target amplitude over a scan, e.g., Swerling models, we particularly consider a distributed hybrid-order Gaussian (DHOG) signal model and develop the generalized likelihood ratio test (GLRT) which relies on the maximum likelihood (ML) estimation of the target amplitude and the residual covariance matrix under the alternative hypothesis. The Cramer-Rao bounds (CRBs) on estimating the target amplitude and residual subspace covariance matrix are derived. Simulation results in both local and distributed scenarios confirm the effectiveness of the proposed GLRT and show improved performance in terms of receiver operating characteristic (ROC) by exploiting the existence of target residual component.

From Large to Vast: Composite Forecasting of Vast-Dimensional Realized Covariance Matrices Using Factor State-Space Models

We propose a dynamic factor state-space model for the prediction of high-dimensional realized covariance matrices of asset returns. Using a block LDL decomposition of the joint covariance matrix of assets and factors, we express the realized covariance matrix of the individual assets similar to an approximate factor model. We model the individual parts, i.e., the factor and residual covariances as well as the factor loadings, independently via a tractable state-space approach. This results in closed-form Matrix-F predictive densities for the distinct covariance elements and Student's t predictive densities for the factor loadings. In an out-of-sample forecasting and portfolio selection exercise we compare the performance of the proposed factor model under different specifications of the residual dynamics. These includes block diagonal residuals based on the GICS sector classifications and strict diagonality assumptions as well as combinations of both using linear shrinkage. We find that the proposed model performs very well in an empirical application to realized covariance matrices for 225 NYSE traded stocks using the well-known Fama-French factors and sector-specific factors represented by Exchange Traded Funds (ETFs).