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"association models"
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Economic reforms in SAARC countries : impact of LPG on development indicators
\"This book presents a cross country comparison of development Indicators in the SAARC Countries with respect to the recent pre - and post- Liberalization, Privatization and globalization (LPG) era. In presenting the empirical analysis using econometric methods the present book brings in the theoretical background relating to the growth of public expenditure as articulated by Adolf Wagner and other researchers in the nineteenth and early twentieth century along with the Displacement Effect Hypothesis as advanced by Peacock and Wiseman in the mid twentieth century. It provides a critical analysis of the theories and views of the researchers on Wagner's law and the subsequent P-W hypothesis. It articulates and re-examines these with respect to the changes in the economic policies and comes up with reinterpretation of the impact using time series analysis. It empirically examines the changes in the structure of the estimated equation by using dummy variables. The study has tried to quantify the impact of the policy changes and has articulated the appropriateness of the use of dummy variable\"-- Provided by publisher.
Book
Row–column interaction models, with an R implementation
We propose a family of models called row–column interaction models (RCIMs) for two-way table responses. RCIMs apply some link function to a parameter (such as the cell mean) to equal a row effect plus a column effect plus an optional interaction modelled as a reduced-rank regression. What sets this work apart from others is that our framework incorporates a very wide range of statistical models, e.g., (1) log-link with Poisson counts is Goodman’s RC model, (2) identity-link with a double exponential distribution is median polish, (3) logit-link with Bernoulli responses is a Rasch model, (4) identity-link with normal errors is two-way ANOVA with one observation per cell but allowing semi-complex modelling of interactions of the form
A
C
T
, (5) exponential-link with normal responses are quasi-variances. Proposed here also is a least significant difference plot augmentation of quasi-variances. Being a special case of RCIMs, quasi-variances are naturally extended from the
M
=
1
linear/additive predictor
η
case (within the exponential family) to the
M
>
1
case (vector generalized linear model families). A rank-1 Goodman’s RC model is also shown to estimate the site scores and optimums of an equal-tolerances Poisson unconstrained quadratic ordination. New functions within the VGAM R package are described with examples. Altogether, RCIMs facilitate the analysis of matrix responses of many data types, therefore are potentially useful to many areas of applied statistics.
Journal Article
Mesalazine solubility in supercritical carbon dioxide with and without cosolvent and modeling
In this study, the solubility of mesalazine in supercritical carbon dioxide with and without cosolvent was carried out for the first time at different temperatures and pressure values ranging from 308 to 338 K and 12 to 30 MPa, respectively. The determined experimental molar solubilities of mesalazine in supercritical carbon dioxide were in the range of 4.41 × 10
–5
to 9.97 × 10
–5
(308 K), 3.9 × 10
–5
to 13.1 × 10
–5
(318 K), 3.4 × 10
–5
to 16 × 10
–5
(328 K) and 3.3 × 10
–5
to 18.4 × 10
–5
(338 K). Meanwhile, the determined experimental molar solubilities in supercritical carbon dioxide using 2% dimethyl sulfoxide as cosolvent were in the range of 28.22 × 10
–5
to 36.2 × 10
–5
(308 K), 26.07 × 10
–5
to 51.41 × 10
–5
(318 K), 25.02 × 10
–5
to 69.07 × 10
–5
(328 K) and 25.86 × 10
–5
to 82.6 × 10
–5
(338 K). A novel association model was employed to simulate the solubility data of the binary and ternary systems. Various semiempirical correlations were utilized to calculate the solubility of mesalazine in supercritical carbon dioxide. The new association model was deemed the most superior model, achieving an average absolute relative deviation value of 4.13% without a cosolvent, and 3.36% when a cosolvent was included.
Journal Article
The Association of Elevated Depression Levels and Life’s Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007–2018
Background and Objective: The association between depression severity and cardiovascular health (CVH) represented by Life’s Essential 8 (LE8) was analyzed, with a novel focus on ranked levels and different ages. Machine learning (ML) algorithms were also selected aimed at providing predictions to suggest practical recommendations for public awareness and clinical treatment. Methods: We included 21,279 eligible participants from the National Health and Nutrition Examination Survey (NHANES) 2007–2018. Weighted ordinal logistic regression (LR) was utilized with further sensitivity and dose–response analysis, and ML algorithms were analyzed with SHapley Additive exPlanations (SHAP) applied to make interpretable results and visualization. Results: Our studies demonstrated an inverse relationship between LE8 and elevated depressive levels, with robustness confirmed through subgroup and interaction analysis. Age‐specific findings revealed middle‐aged and older adults (aged 40–60 and over 60) which showed higher depresion severity, highlighting the need for greater awareness and targeted interventions. Eight ML algorithms were selected to provide predictive results, and further SHAP would become ideal supplement to increase model interpretability. Conclusions: Our studies demonstrated a negative association between LE8 and elevated depressive levels and provided a suite of ML predictive models, which would generate recommendations toward clinical implications and subjective interventions.
Journal Article
Association mapping of local climate-sensitive quantitative trait loci in Arabidopsis thaliana
Flowering time (FT) is the developmental transition coupling an internal genetic program with external local and seasonal climate cues. The genetic loci sensitive to predictable environmental signals underlie local adaptation. We dissected natural variation in FT across a new global diversity set of 473 unique accessions, with >12,000 plants across two seasonal plantings in each of two simulated local climates, Spain and Sweden. Genome-wide association mapping was carried out with 213,497 SNPs. A total of 12 FT candidate quantitative trait loci (QTL) were fine-mapped in two independent studies, including 4 located within ±10 kb of previously cloned FT alleles and 8 novel loci. All QTL show sensitivity to planting season and/or simulated location in a multi-QTL mixed model. Alleles at four QTL were significantly correlated with latitude of origin, implying past selection for faster flowering in southern locations. Finally, maximum seed yield was observed at an optimal FT unique to each season and location, with four FT QTL directly controlling yield. Our results suggest that these major, environmentally sensitive FT QTL play an important role in spatial and temporal adaptation.
Journal Article
Candidate transdiagnostic processes linking potentially traumatic experiences to psychopathology, mental well-being and resilience in emerging adults
Background
Potentially traumatic events (PTEs), including socially contextualized adversities such as exclusion and discrimination, are common at the population level and associated with diverse mental health outcomes. Transdiagnostic process variables may help characterize how PTE indicators and different mental health outcomes are interrelated beyond disorder-specific frameworks.
Methods
We analyzed data from a population-based sample of 3,051 emerging adults to examine whether theoretically informed transdiagnostic psychological processes were statistically positioned between PTE indicators and mental health outcomes, including psychopathology, positive mental health, and resilience. Guided by transdiagnostic and dimensional frameworks, we used structural equation models to estimate cross-sectional indirect associations.
Results
Three higher-order transdiagnostic factors—cognitive-focused, emotional-focused, and social-focused processes—were identified. Emotional-focused processes accounted for the largest proportion of cross-sectional indirect associations across mental health outcomes. Among the examined PTEs, social exclusion showed the strongest and most consistent associations with mental health. Overall, the models accounted for more variance in internalizing symptoms than in externalizing symptoms or positive mental health outcomes.
Conclusions
These findings highlight the relevance of candidate transdiagnostic process variables for understanding mental health following exposure to adversity. By incorporating positive mental health and resilience outcomes, the results underscore the potential of targeting shared psychological processes—particularly emotional-focused mechanisms—for assessment, prevention, and early intervention in psychiatric practice.
Clinical trial number
Not applicable.
Journal Article
GEE for Multinomial Responses Using a Local Odds Ratios Parameterization
In this article, we propose a generalized estimating equations (GEE) approach for correlated ordinal or nominal multinomial responses using a local odds ratios parameterization. Our motivation lies upon observing that: (i) modeling the dependence between correlated multinomial responses via the local odds ratios is meaningful both for ordinal and nominal response scales and (ii) ordinary GEE methods might not ensure the joint existence of the estimates of the marginal regression parameters and of the dependence structure. To avoid (ii), we treat the so-called \"working\" association vector α as a \"nuisance\" parameter vector that defines the local odds ratios structure at the marginalized contingency tables after tabulating the responses without a covariate adjustment at each time pair. To estimate α and simultaneously approximate adequately possible underlying dependence structures, we employ the family of association models proposed by Goodman. In simulations, the parameter estimators with the proposed GEE method for a marginal cumulative probit model appear to be less biased and more efficient than those with the independence \"working\" model, especially for studies having time-varying covariates and strong correlation.
Journal Article
Where the wild things are: predicting hotspots of seabird aggregations in the California Current System
Marine Protected Areas (MPAs) provide an important tool for conservation of marine ecosystems. To be most effective, these areas should be strategically located in a manner that supports ecosystem function. To inform marine spatial planning and support strategic establishment of MPAs within the California Current System, we identified areas predicted to support multispecies aggregations of seabirds (\"hotspots\"). We developed habitat-association models for 16 species using information from at-sea observations collected over an 11-year period (1997-2008), bathymetric data, and remotely sensed oceanographic data for an area from north of Vancouver Island, Canada, to the USA/Mexico border and seaward 600 km from the coast. This approach enabled us to predict distribution and abundance of seabirds even in areas of few or no surveys. We developed single-species predictive models using a machine-learning algorithm: bagged decision trees. Single-species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas (\"core areas\") to individual species, and (3) predicted persistence of hotspots across years. Model predictions were applied to the entire California Current for four seasons (represented by February, May, July, and October) in each of 11 years. Overall, bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Predicted hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank), Southern California (adjacent to the Channel Islands), and adjacent to Vancouver Island, British Columbia, that are not currently included in protected areas. Prioritization and identification of multispecies hotspots will depend on which group of species is of highest management priority. Modeling hotspots at a broad spatial scale can contribute to MPA site selection, particularly if complemented by fine-scale information for focal areas.
Journal Article
ϕ-Divergence in Contingency Table Analysis
The ϕ -divergence association models for two-way contingency tables is a family of models that includes the association and correlation models as special cases. We present this family of models, discussing its features and demonstrating the role of ϕ -divergence in building this family. The most parsimonious member of this family, the model of ϕ -scaled uniform local association, is considered in detail. It is implemented and representative examples are commented on.
Journal Article
Gaussian Mixture Model‐Based Data Association Incorporating a Deep Learning Network for Multivehicle Tracking and Detection in Autonomous Driving Systems
In autonomous driving systems, 2D and 3D object detection and tracking demand accurate detection, robust affinity computation, and efficient data association in real‐time environments. This article presents a deep learning‐based multivehicle tracking and detection framework that fuses light detection and ranging (LiDAR) and camera data for simultaneous detection and tracking. The proposed system integrates a Gaussian mixture model‐based data association and performs object detection and correlation using 2D images and 3D point cloud inputs. A key contribution of this work is a robust affinity computation module that effectively handles multiple occlusions and models object appearance and motion in 3D space. Additionally, the framework introduces a joint data association strategy that optimizes affinity scores, detection confidence, and start‐end probabilities. Extensive experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute car tracking benchmark demonstrate that the proposed method achieves real‐time performance and superior tracking accuracy, outperforming multiple state‐of‐the‐art LiDAR‐camera fusion methods, including the joint multiobject detection and tracking baseline by up to 1.69% in multiobject tracking precision and 0.10% in multiobject tracking accuracy, while also achieving more stable trajectories and fewer identity switches than boost correlation multiobject detection and tracking. This study introduces a real‐time light detection and ranging‐camera fusion framework for vehicle detection and tracking. Using a Gaussian mixture model‐based association and improved affinity metrics, the method enhances tracking reliability in dynamic conditions. Evaluations on the Karlsruhe Institute of Technology and Toyota Technological Institute benchmark confirm gains in accuracy over established joint detection and tracking methods.
Journal Article