Covariance Structure Research Articles

ALAAMEE: Open-source software for fitting autologistic actor attribute models

The autologistic actor attribute model (ALAAM) is a model for social influence, derived from the more widely known exponential-family random graph model (ERGM). ALAAMs can be used to estimate parameters corresponding to multiple forms of social contagion associated with network structure and actor covariates. This work introduces ALAAMEE, open-source Python software for estimation, simulation, and goodness-of-fit testing for ALAAM models. ALAAMEE implements both the stochastic approximation and equilibrium expectation (EE) algorithms for ALAAM parameter estimation, including estimation from snowball sampled network data. It implements data structures and statistics for undirected, directed, and bipartite networks. We use a simulation study to assess the accuracy of the EE algorithm for ALAAM parameter estimation and statistical inference, and demonstrate the use of ALAAMEE with empirical examples using both small (fewer than 100 nodes) and large (more than 10 000 nodes) networks.

NetQDA: Local Network-Guided High-Dimensioanl Quadratic Discriminant Analysis

Quadratic Discriminant Analysis (QDA) is a well-known and flexible classification method that considers differences between groups based on both mean and covariance structures. However, the connection structures of high-dimensional predictors are usually not explicitly incorporated into modeling. In this work, we propose a local network-guided QDA method that integrates the local connection structures of high-dimensional predictors. In the context of gene expression research, our method can identify genes that show differential expression levels as well as gene networks that exhibit different connection patterns between various biological state groups, thereby enhancing our understanding of underlying biological mechanisms. Extensive simulations and real data applications demonstrate its superior performance in both feature selection and outcome classification compared to commonly used discriminant analysis methods.

Objective Bayesian Model Selection for Spatial Hierarchical Models with Intrinsic Conditional Autoregressive Priors

We develop Bayesian model selection via fractional Bayes factors to simultaneously assess spatial dependence and select regressors in Gaussian hierarchical models with intrinsic conditional autoregressive (ICAR) spatial random effects. Selection of covariates and spatial model structure is difficult, as spatial confounding creates a tension between fixed and spatial random effects. Researchers have commonly performed selection separately for fixed and random effects in spatial hierarchical models. Simultaneous selection methods relieve the researcher from arbitrarily fixing one of these types of effects while selecting the other. Notably, Bayesian approaches to simultaneously select covariates and spatial model structure are limited. Our use of fractional Bayes factors allows for selection of fixed effects and spatial model structure under automatic reference priors for model parameters, which obviates the need to specify hyperparameters for priors. We also show the equivalence between two ICAR specifications and derive the minimal training size for the fractional Bayes factor applied to the ICAR model under the reference prior. We perform a simulation study to assess the performance of our approach and we compare results to the Deviance Information Criterion and Widely Applicable Information Criterion. We demonstrate that our fractional Bayes factor approach assigns low posterior model probability to spatial models when data is truly independent and reliably selects the correct covariate structure with highest probability within the model space. Finally, we demonstrate our Bayesian model selection approach with applications to county-level median household income in the contiguous United States and residential crime rates in the neighborhoods of Columbus, Ohio.

Advanced Methods for MLE of Toeplitz Structured Covariance Matrices With Applications to Radar Problems

Advanced Methods for MLE of Toeplitz Structured Covariance Matrices With Applications to Radar Problems

Structural MRI of brain similarity networks.

Recent advances in structural MRI analytics now allow the network organization of individual brains to be comprehensively mapped through the use of the biologically principled metric of anatomical similarity. In this Review, we offer an overview of the measurement and meaning of structural MRI similarity, especially in relation to two key assumptions that often underlie its interpretation: (i) that MRI similarity can be representative of architectonic similarity between cortical areas and (ii) that similar areas are more likely to be axonally connected, as predicted by the homophily principle. We first introduce the historical roots and technical foundations of MRI similarity analysis and compare it with the distinct MRI techniques of structural covariance and tractography analysis. We contextualize this empirical work with two generative models of homophilic networks: an economic model of cost-constrained connectional homophily and a heterochronic model of ontogenetically phased cortical maturation. We then review (i) studies of the genetic and transcriptional architecture of MRI similarity in population-averaged and disorder-specific contexts and (ii) developmental studies of normative cohorts and clinical studies of neurodevelopmental and neurodegenerative disorders. Finally, we prioritize knowledge gaps that must be addressed to consolidate structural MRI similarity as an accessible, valid marker of the architecture and connectivity of an individual brain network.

Genetic analysis and hybrid performance of recommended cocoa (Theobroma cacao L.) clones for bean yield in Ghana

Abstract Attempts to develop high yielding varieties in Ghana have mostly relied on the introduction of new clones to broaden the range of planting materials for yield improvement. The objective of this study was to estimate the genetic variation and heritability for bean yield of six recommended cocoa clones using these as males in crosses with five seed garden parents. Twenty-four families obtained from a 5 × 6 North Carolina II (NC II) incomplete factorial mating design together with 19 high yielding single crosses, of which four were standard mixed hybrids, were planted in a randomized complete block design with four replications and evaluated for bean yield over 6 years. To account for the serial correlation among yield data collected from the same plants over years, six models with different covariance structures were tested. The general covariance model emerged appropriate based on Akaike information criterion values with significant (P < 0.01) family × year interaction. Average bean yield was highest in the NC II families followed by the specific crosses then the standard mixed hybrids. Combining ability analysis among the NC II was significant for female, male and female × male interaction along with a narrow-sense heritability of 0.26. Clone CRG 6035 among the males which had good general combining ability could be added to the seed garden parents, while the promising hybrids (NA 33 × SCA 9, SCA 6 × Pound 10, T60/887 × PA 121 and T79/501 × CRG 6035/103) undergo stability tests before release.

Neuroanatomy, episodic memory and inhibitory control of persian-kurdish simultaneous bilinguals

We assessed simultaneous bilinguals and monolinguals on inhibitory control and episodic memory, and assessed their grey matter volumes in brain regions known to be involved in language processing, executive control and memory. Bilinguals outperformed monolinguals on episodic memory, and performance on the memory and inhibition tasks were correlated, only in the bilingual group. This suggests that the bilingualism-related benefits on memory are related to individual differences in executive control. We found larger grey matter volumes in bilinguals in left pars opercularis and in bilateral SFG, caudate nuclei, and parasubiculum. Episodic memory performance was correlated with volumes of bilateral posterior hippocampi, again only in the bilinguals, again suggesting that bilingualism may be driving this effect. Finally, we found positive structural covariance between the volumes of the bilateral parasubiculum and that of important components of the executive control network. We provide a novel, mechanistic explanation accounting for observed behavioural advantage and brain structural differences: bilingualism may boost the prefrontal cortex-hippocampal neural circuitry commonly underlying both executive control and memory, via cascade and reverberant effects, leading to synergistic benefits in both cognitive domains. This new framework has important implications for protective effects on cognition and brain health in relation to second language learning.

Comparison Between Conventional and Artificial Intelligence-Assisted Setup for Digital Implant Planning: Accuracy, Time-Efficiency, and User Experience.

To investigate the reliability and time efficiency of the conventional compared to the automatic artificial intelligence (AI) segmentation of the mandibular canal and registration of the CBCT with the model scan data, in relation to clinician's experience. Twenty clinicians, 10 with a moderate and 10 with a high experience in computer-assisted implant planning, were asked to perform a bilateral localization of the mandibular canal, followed by a registration of the intraoral model scan with the CBCT. Subsequently, for each data set and each participant, the same operations were performed utilizing the AI tool. Statistical significance was assessed via a mixed model (using the PROC MIXED statement and the compound symmetry covariance structure). The mean time for the segmentation of the mandibular canals and the registration of the models was 4.75 (2.03)min for the manual and 2.03 (0.36) min for the AI-automated operations (p < 0.001). The mean discrepancy in the mandibular canals was 0.71 (1.80) mm RMS error for the manual segmentation and 0.68 (0.36) RMS error for the AI-assisted segmentation (p > 0.05). For the registration between the CBCT and the intraoral scans, the mean discrepancy was 0.45 (0.16) mm for the manual and 0.37 (0.07) mm for the AI-assisted superimposition (p > 0.05). AI-automated implant planning tools are feasible options that can lead to a similar or better accuracy compared to the conventional manual workflow, providing improved time efficiency for both experienced and less experienced users. Further research including a variety of software and data sets is required to be able to generalize the outcomes of the present study.

Adolescent Depressive Symptoms and Peer Dynamics: Distorted Perceptions in Liking and Disliking Networks.

Depression in adolescents has been linked to poor life outcomes, including suicidal ideation, peer victimization, and fewer friendships. Less is known about how depressed adolescents perceive their peer interactions. Based on the depression-distortion model, we expected that adolescents with depressive symptoms misperceive their social ties by being less likely to like some peers, and more likely to dislike other peers. An Italian dataset about adolescent relationships was used, including 275 first-year secondary school students (M age = 11.80, 46% female) in 12 classrooms across nine schools. Adolescents were asked to nominate classmates they liked and disliked. Longitudinal social network analyses (stochastic actor-oriented models) were conducted, including structural network effects (reciprocity, transitivity, indegree-popularity) and covariates such as gender, immigrant origin, and highest parents' education level. The results indicated that adolescents with depressive symptoms were less likely to send liking nominations, and conversely, they were more likely to send disliking nominations than non-depressed classmates. Interestingly, adolescents with depressive symptoms were not more disliked or less liked by their peers. These findings seem to support the depression-distortion model by suggesting that, compared to non-depressed peers, adolescents with depressive symptoms misperceive their relationships by overstating negative relationships and underestimating positive ones.

The influence of institutional context in the relationship between green management, perceived barriers to sustainability and innovation performance in SMEs

PropósitoEste estudio examina la relación entre la gestión verde, las barreras percibidas a la sostenibilidad y el rendimiento de la innovación en las PYME latinoamericanas y cómo los factores institucionales formales e informales influyen en esta relación.Diseño/metodología/enfoqueEl estudio utilizó un conjunto de datos recogidos por el Observatorio Iberoamericano de la PYME y se incluyó 15 indicadores de desempeño de países de América Latina publicados en fuentes de datos internacionales para abordar los contextos institucionales formales e informales. Por consiguiente, la muestra de datos final del estudio lo conforman 11,319 observaciones de Pymes de 16 países. El análisis incluyó tres fases. En primer lugar, un análisis factorial exploratorio. Segundo un análisis de conglomerados o clústeres. En tercer lugar, una prueba del modelo teórico de mediación entre los grupos identificados en el segundo estudio. Para ello, el análisis incluyó un análisis multigrupo para la estructura de covarianza mediante un procedimiento de tres etapas por medio de un modelado de ecuaciones estructurales.ResultadosLos resultados revelaron que tanto en las instituciones formales (como las políticas y normativas gubernamentales) como en las informales (como las normas sociales y los valores culturales) las prácticas de gestión verde influyen positivamente en desempeño innovador. Sin embargo, la magnitud de este impacto varía según el nivel de desempeño del contexto de estos países. Además, el estudio mostró que las barreras percibidas a la sostenibilidad no tienen un papel de mediación en esta relación, en ambos contextos institucionales. Finalmente, se observó que el efecto de las barreras percibidas a la sostenibilidad sobre el desempeño innovador disminuye en el contexto institucional informal.OriginalidadEl trabajo aporta valiosas ideas para los responsables políticos que buscan promover el desarrollo sostenible en América Latina, destacando la importancia del apoyo institucional para fomentar la adopción de prácticas verdes por parte de las pymes.

The heat modulated infinite dimensional Heston model and its numerical approximation

The HEat modulated Infinite DImensional Heston (HEIDIH) is introduced as a concrete case of the general framework of infinite dimensional Heston stochastic volatility models of (F.E. Benth, I.C. Simonsen '18) for the pricing of forward contracts. It is supported by a comprehensive numerical analysis. The model consists of a one-dimensional stochastic advection equation coupled with a stochastic volatility process. This process is a Cholesky-type decomposition of the tensor product of a Hilbert-space valued Ornstein-Uhlenbeck process, the solution to a stochastic heat equation on the real half-line. The advection and heat equations are driven by independent space-time Gaussian processes which are white in time and coloured in space, with the latter covariance structure expressed by two different kernels. First, a class of weight-stationary kernels are given, under which regularity results for the HEIDIH model in fractional Sobolev spaces are formulated. In particular, the class includes weighted Matérn kernels. Second, numerical approximation of the model is considered. An error decomposition formula, pointwise in space and time, for a finite-difference scheme is proven. For a special case, essentially sharp convergence rates are obtained when this is combined with a fully discrete finite element approximation of the stochastic heat equation. The analysis takes into account a localization error, a pointwise-in-space finite element discretization error and an error stemming from the noise being sampled pointwise in space. The rates obtained in the analysis are higher than what would be obtained using a standard Sobolev embedding technique. Numerical simulations illustrate the results.

Clinical Sentiment Analysis by Large Language Models Enhances the Prediction of Hepatorenal Syndrome in Decompensated Cirrhosis.

Hepatorenal syndrome - Acute Kidney Injury (HRS-AKI) is a severe complication of decompensated cirrhosis that is challenging to predict. Sentiment analysis, a computational process of identifying and categorizing opinions and judgment expressed in text, may enhance traditional prediction methodologies based on structured variables. Large language models (LLMs), such as generative pretrained transformers (GPTs), have demonstrated abilities to perform sentiment analyses on non-clinical texts. We sought to determine if GPT-performed sentiment analysis could improve upon predictions using clinical covariates alone in the prediction of HRS-AKI. Adult patients admitted to a single academic medical center with decompensated cirrhosis and AKI. We used a protected health information (PHI) compliant version of Microsoft Azure OpenAI GPT-4o to derive a sentiment score ranging from 0 to 1 for HRS-AKI, and conduct natural language processing (NLP) extraction of clinical terms associated with HRS-AKI in clinical notes. The area under the receiver operator curve (AUROC) was compared in logistic regression models incorporating structured variables (socio-demographics, MELD 3.0, hemodynamic parameters) with compared to without sentiment scores and NLP-extracted clinical terms. In our cohort of 314 participants, higher sentiment score was associated with the diagnosis of HRS-AKI (OR 1.33 per 0.1, 95% CI 1.02-1.79) in multivariate models. AUROC of the baseline model using structured clinical covariates alone was 0.639. With the addition of the GPT-4o derived sentiment score and clinical terms to structured covariates, the final model yielded an improved AUROC of 0.758 (p= 0.03). Clinical texts contain large amounts of data that are currently difficult to extract using standard methodologies. Sentiment analysis and NLP-based variable derivation with GPT-4o in clinical application is feasible and can improve the prediction of HRS-AKI over traditional modeling methodologies alone.

Geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with bias-adjusted covariance estimators

BackgroundThe generalized estimating equation (GEE) method is widely used for analyzing longitudinal and clustered data. Although the GEE estimate for regression coefficients and sandwich covariance estimate are consistent regardless of the choice of covariance structure, they are generally biased for small sample sizes. Various researchers have proposed modified GEE methods and covariance estimators to handle small-sample bias.ResultsWe briefly present bias-corrected and penalized GEE methods, along with 11 bias-adjusted covariance estimators. In addition, we focus on analyzing longitudinal or clustered data with binary outcomes using the logit link function and introduce package geessbin in R to implement conventional and modified GEE methods with bias-adjusted covariance estimators. Finally, we illustrate the implementation and detail a usage example of the package. The package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/geessbin/index.html.ConclusionsThe geessbin package provides three GEE estimates with numerous covariance estimates. It is useful for analyzing correlated data such as longitudinal and clustered data. Additionally, the geessbin is designed to be user-friendly, making it accessible to non-statisticians.

Cortical structural network characteristics in non-cognitive impairment end-stage renal disease.

Explore alterations in topological features of gray matter volume (GMV) and structural networks in non-cognitive impairment end-stage renal disease (Non-CI ESRD). Utilizing graph theory, we collected structural magnetic resonance imaging (sMRI) data from 38 Non-CI ESRD patients and 50 normal controls (NC). We compared, and extracted the GMV across subject groups, constructed corresponding structural covariance networks (SCNs), and investigated the alterations in SCNs feature parameters between groups. In Non-CI ESRD patients, The GMV were reduced in several brain regions, predominantly on the left side (p < 0.05, FWE correction). The small-world network characteristics of the patient group's brain networks showed a tendency toward regular. In a few densities, global network parameters, transitivity, (p < 0.05) was significantly increased in the ESRD group. Regional network measurements revealed inconsistent changes in regional efficiency across different brain areas. In the analysis of network hubs, the right temporal pole is likely a compensatory hub for Non-CI ESRD patients. The SCNs in Non-CI ESRD patients demonstrated reduced topological stability against targeted attacks. This study reveals that patients with renal failure exhibited subtle changes in brain network characteristics even before a decline in cognitive scores. These changes involve compensatory activation in certain brain regions, which enhances network transitivity to maintain the efficiency of whole-brain network information integration without significant loss. Additionally, the SCNs characteristics can serve as a neuroanatomical marker for brain alterations in Non-CI ESRD patients, offering new insights into the mechanisms of early brain injury in ESRD patients.

Change point detection in high dimensional covariance matrix using Pillai’s statistics

AbstractThis research proposes a method to test and estimate change points in the covariance structure of high-dimensional multivariate series data. Our method uses the trace of the beta matrix, known as Pillai’s statistics, to test the change in covariance matrix at each time point. We study the asymptotic normality of Pillai’s statistics for testing the equality of two covariance matrices when both sample size and dimension increase at the same rate. We test the existence of a single change point in a given time period using Cauchy combination test, the test using an weighted sum of Cauchy transformed p-values, and estimate the change point as the point whose statistic is the greatest. To test and estimate multiple change points, we use the idea of the wild binary segmentation and repeatedly apply the procedure for a single change point to each segmented period until no significant change point exists. We numerically provide the size and power of our method. We finally apply our procedure to finding abnormal behavior in the investment of a private equity fund.

Alternating and Modified Alternating Least Squares Applied to Raman Spectra of Finished Gasolines.

Extraction of components from individual refinery streams (e.g., reformates and alkylates) in finished gasoline was undertaken using Raman spectroscopy to characterize the chemical content of the finished product. Modified alternating least squares (MALS) was used for separating Raman spectroscopic data sets of the finished product into its pure individual components. The advantages of MALS over alternating least squares (ALS) for multicomponent resolution are highlighted in this study using three Raman spectroscopic data sets which provide a suitable benchmark for comparing the performance of these two methods. MALS is superior to ALS in terms of accuracy and can better resolve components than ALS, and it is also more robust toward collinear data. Finally, components near the noise level usually cannot be extracted by ALS because of instability when inverting the covariance structure which inflates the noise present in the data. However, these same components can be extracted by MALS due to the stabilization of the least squares regression with respect to the matrix inversion using modified techniques from ridge regression.

A cross-species analysis of neuroanatomical covariance sex difference in humans and mice.

Structural covariance is a potent readout of coordinated brain development, but hard to probe experimentally. Here we use sex differences as a naturally occurring test for developmental theories of structural covariance - adopting a cross-species approach for validation and translational benefit. Brain MRI reveals two conserved features of anatomical covariance across humans and mice: (i) tighter inter-regional coordination of brain development in females as evidenced by stronger volume covariance; (ii) a tendency for female-biased covariance to involve regions that are smaller in females - suggesting an unknown counterbalancing between these two distinct modes of sex-biased brain organization. These findings advance understanding of coordinated brain development and sex difference in a cross-species framework - facilitating future translational research on both topics.

Megavariate Methods Capture Complex Genotype-by-Environment Interactions.

Genomic prediction models that capture genotype-by-environment interaction are useful for predicting site-specific performance by leveraging information among related individuals and correlated environments, but implementing such models is computationally challenging. This study describes the algorithm of these scalable approaches, including two models with latent representations of genotype-by-environment interactions, namely MegaLMM and MegaSEM, and an efficient multivariate mixed model solver, namely PEGS, fitting different covariance structures (unstructured, XFA, HCS). Accuracy and runtime are benchmarked on simulated scenarios with varying numbers of genotypes and environments. MegaLMM and PEGS-based XFA and HCS models provided the highest accuracy under sparse testing with 100 testing environments. PEGS-based unstructured model was orders of magnitude faster than REML-based multivariate GBLUP while providing the same accuracy. MegaSEM provided the lowest runtime, fitting a model with 200 traits and 20,000 individuals in approximately 5 minutes, and a model with 2,000 traits and 2,000 individuals in less than 3 minutes. With the G2F data, the most accurate predictions were attained with the univariate model fitted across environments and by averaging environment-level GEBVs from models with HCS and XFA covariance structures.

High-Dimensional Block Diagonal Covariance Structure Detection Using Singular Vectors

The assumption of independent subvectors arises in many aspects of multivariate analysis. In most real-world applications, however, we lack prior knowledge about the number of subvectors and the specific variables within each subvector. Yet, testing all these combinations is not feasible. For example, for a data matrix containing 15 variables, there are already 1 382 958 545 possible combinations. Given that zero correlation is a necessary condition for independence, independent subvectors exhibit a block diagonal covariance matrix. This paper focuses on the detection of such block diagonal covariance structures in high-dimensional data and therefore also identifies uncorrelated subvectors. Our approach exploits the fact that the structure of the covariance matrix is mirrored by the structure of its eigenvectors. However, the true block diagonal structure is masked by noise in the sample case. To address this problem, we propose to use sparse approximations of the sample eigenvectors to reveal the sparse structure of the population eigenvectors. Notably, the right singular vectors of a data matrix with an overall mean of zero are identical to the sample eigenvectors of its covariance matrix. Using sparse approximations of these singular vectors instead of the eigenvectors makes the estimation of the covariance matrix obsolete. We demonstrate the performance of our method through simulations and provide real data examples. Supplementary materials for this article are available online.

Altered structural covariance of the cortex and hippocampal formation in patients with lung cancer after chemotherapy

ObjectiveIn this retrospective study, we aimed to investigate changes in brain morphology and structural topological networks in patients with lung cancer (LC) with or without chemotherapy. MethodsWe retrospectively recruited 191 participants for this cross-sectional study, including 113 patients with LC without chemotherapy (Ch-), 38 patients with LC with chemotherapy (Ch+), and 40 healthy controls (HC) matched for age, sex, and education. The gray matter volume (GMV) and cortical properties were compared among the three groups. We constructed the structural covariant network (SCN) based on cortical thickness, volumes of subcortical structures, and volumes of hippocampal subfields and the amygdala in all participants. The global and nodal topological properties of SCN were compared among groups. In addition, 23 patients with LC (8 Ch+ and 15 Ch-) who received two identical brain magnetic resonance scans were enrolled in the follow-up study. The paired t-test was used to compare group differences in brain morphology and topological properties in the structural network. ResultsThe GMV of the bilateral caudate and thalamus were smaller in the Ch- and Ch+ groups compared to the HC group using threshold-free cluster enhancement and permutation (P < 0.05, 5000 times permutations) for multiple comparison correction. The cortical SCN analysis suggested multiple enhanced nodal properties in several brain areas in Ch+ and Ch- compared to HC, mainly in the temporal gyrus, using permutations test and false discovery rate (FDR) (P < 0.05) corrections. Moreover, an increased sigma was found in the Ch+ compared with HC (P = 0.0238). The reduced nodal degree (P = 0.0002) and betweenness (P = 0.0008) in the right amygdala of Ch+ compared to HC were detected by subcortical SCN analysis. Furthermore, reduced gamma (P = 0.0342) and sigma (P < 0.0001) were found in Ch- compared with HC in the SCN analysis of subfields of the amygdala-hippocampal complex. In the follow-up study, reduced nodal degree (P < 0.0001) was found in the right anterior amygdala, and reduced clustering coefficient and local efficiency were found in patients with LC after the permutation test. ConclusionsOur study showed GMV defects and structural topological property abnormalities related to LC and chemotherapy. Such morphological changes associated with LC and chemotherapy could be used as imaging markers for clinical assessments and pathological indicators.