Estimation Of Distribution Research Articles

Core Concepts in Pharmacoepidemiology: Time-To-Event Analysis Approaches in Pharmacoepidemiology.

This article provides an overview of time-to-event (TTE) analysis in pharmacoepidemiology. The key concept of censoring is reviewed, including right-, left-, interval- and informative censoring. Simple descriptive statistics are explained, including the nonparametric estimation of the TTE distribution as per Kaplan-Meier method, as well as more complex TTE regression approaches, including the parametric Accelerated Failure Time (AFT) model and the semi-parametric Cox Proportional Hazards and Restricted Mean Survival Time (RMST) models. Additional approaches and various TTE model extensions are presented as well. Finally, causal inference for TTE outcomes is addressed. A thorough review of the available concepts and methods outlines the immense variety of available and useful TTE models. There may be underused TTE concepts and methods, which are highlighted to raise awareness for researchers who aim to apply the most appropriate TTE approach for their study. This paper constitutes a modern summary of TTE analysis concepts and methods. A curated list of references is provided.

6.G. Scientific session: The importance of using country-specific severity distributions in burden of disease indicators

Abstract One of the most commonly used health gap summary measures in the field of public health is the Disability-adjusted life year (DALY). It has become the key metric for quantifying the burden of disease (BoD) and is used as an information source for setting priorities for health care and health service planning. The DALY metric quantifies gaps between a theoretical population in perfect health and the current health status related to both morbidity and mortality. It is the sum of the number of healthy life years lost due to disability (YLD) and death (YLL). The YLD summarises the loss of healthy life time using the frequency of diseases differentiated by severities or related health states (including sequelae). Depending on each health state, it converts the time spent with diseases into YLD by weighting each year with health limitation with so-called disability weights. This allows the two indicators YLL and YLD to be added to the DALY. Consequently, severity distributions are a means of describing the range of health loss along specific health states, which enables an accurate mapping with disability weights. For most of the diseases, these distributions are usually categorised as mild, moderate, severe, or no health loss (asymptomatic). Conditions with higher severity levels contribute more to the YLD and thus, to the overall disease specific BoD. The severity distributions are highly dependent on utilization at the individual level, but also on available health care services at the society level, which indicates that differences by settings are to be expected. Changes over time in the share of severe cases, for instance, may reflect the effectiveness of health interventions and policies. This underlines the importance of severity distributions, as they also reflect information about the quality of healthcare for those affected. The Global Burden of Disease (GBD) study applies for a significant proportion of their considered diseases uniform severity distributions across countries and broader regions. They acknowledge concerns over applying estimates of severity distributions based on limited data, noting that it is the only available information that they were able to use. As the YLD estimates are often utilized to develop and prioritize health policy interventions, there is a necessity for country-specific estimates that are evidence based and less susceptible to model assumptions. In most cases, the estimation of severity distributions involves complex methodology as data for such assessments are very limited or hardly reliable. In the following workshop, general concepts and country cases from Germany, Scotland, and Belgium will be presented covering a variety of diseases. The examples underline the importance of estimating national and even regional severity distributions when identifying the importance of diseases or comparing the health conditions of populations across time. Key messages • As the Burden of disease indicators are predominantly used to prioritize health interventions and allocate resources, it is crucial to use accurate information in the severity levels of disease. • Severity distributions reflect the health care services and vary across different settings. Utilising country-specific severity distributions when estimating Burden of Disease indicators is essential.

Presence-only fisheries bycatch data produce biased species distribution predictions for Alcyonacean corals on British Columbia’s continental shelf and slope

Sedentary benthic species such as Alcyonacea corals form critical habitat for fishes and invertebrates. Assessing anthropogenic risks to these organisms requires unbiased, species distribution models (SDMs) that attempt to map probabilities of coral presence in relation to bio-physical ocean characteristics; however, in deep-water settings, the accuracy of SDMs is highly variable and dependent on spatial and taxonomic resolution. Here we investigated how data and model types affect SDM predictions of Alcyonacea probability of presence. We compared predictions from generalized additive models (GAMs) fitted to presence-absence observations over a stratified-random survey design with predictions from maximum entropy models (Maxent) fitted to presence-only bycatch records from commercial fisheries. We use a simulation analysis to show that both model types (i.e., GAM or Maxent) using presence-only bycatch data produced biased estimates of species distribution. Additionally, the maxent model fit using presence-only bycatch data produced biased estimates of performance metrics (AUC, TSS) that were overly optimistic. This study demonstrates a need for presence-absence data collected using a robust survey sampling design to fit SDMs that will better inform marine protected area placement while minimizing unnecessary economic losses, such as forgone fishing yield, due to sub-optimal placement.

A novel method to estimate the 3D chlorophyll a distribution in the South China Sea surface waters using hydrometeorological parameters

Chlorophyll a (Chl-a) is a key indicator of marine ecosystems, and certain hydro-meteorological parameters (HMPs) are highly correlated with its fluctuations. Here, relevant and accessible HMPs were used as inputs, combined with machine learning (ML) algorithms for estimating 3D Chl-a in the South China Sea (SCS). With the inputs of temperature, salinity, depth, wind speed, wind direction, sea surface pressure, and relative humidity, the LightGBM-based model performed well, achieving high R2 values of 0.985 and 0.789 in validation and testing sets, respectively. Based on a large number of in situ measurements, this model enables the estimation of the 3D distribution of summer Chl-a in the SCS over the past fifteen years using a 3D hydrographic dataset combined with surface meteorological parameters. The results show that the 3D distribution of the model estimated Chl-a is characterized similarly to the previous studies and can capture the effect of hydro-meteorological conditions on Chl-a distribution. The environmental variables affecting Chl-a were considered more comprehensively in this study, and the methodological framework has the potential to be applied to the low-cost monitoring of the remaining water quality parameters.

Estimation of Free-Product PCE Distribution in Thick Multilayered Aquifers as Possible Long-Term Pollution Sources for Shallow and Deep Groundwaters, Using High-Precision Numerical Simulations

Estimation of Free-Product PCE Distribution in Thick Multilayered Aquifers as Possible Long-Term Pollution Sources for Shallow and Deep Groundwaters, Using High-Precision Numerical Simulations

Development of electromagnetic pollution maps utilizing Gaussian process spatial models

The rapid proliferation of wireless technologies in everyday environments demands the quick and precise estimation of electromagnetic field distribution. This distribution is commonly depicted through the electric field strength across various geographical areas. The objective of this research is to determine the most effective geospatial model for generating a national-level electric field strength map within the 30 MHz–6 GHz frequency range. To achieve this, we employed five different methodologies for constructing the electric field strength map. Four of these methodologies are based on Gaussian process regression, while the fifth utilizes the classical weighted-average method of the nearest neighbor. Our study focused on a country with a total area of 9251 km2, using a dataset comprising 3621 measurements. The findings reveal that Gaussian process spatial models, also known as Kriging models, generally outperform other methods when applied to spatial data. However, it was observed that, after excluding some outlier data points, the performance of the classical nearest neighbor models becomes comparable to that of the Gaussian process models. This indicates the potential for both approaches to be effective, depending on the data quality and the presence of outliers.

Ridge-type covariance and precision matrix estimators of the multivariate normal distribution

AbstractWe consider ridge-type estimation of the multivariate normal distribution’s covariance matrix and its inverse, the precision matrix. While several ridge-type covariance and precision matrix estimators have been presented in the literature, their respective inverses are often not considered as precision and covariance matrix estimators even though their estimands are one-to-one related through the matrix inverse. We study which estimator is to be preferred in what case. Hereto we compare the ridge-type covariance matrix estimators and their properties to that of the inverse of the ridge-type precision matrix estimators, and vice versa. The comparison, in which we take all ridge-type estimators along, is limited to a specific case that is illustrative of the difference between the covariance and precision matrix estimators. The comparison addresses the estimators’ estimating equation, analytic expression, analytic properties like positive definiteness and penalization limit, mean squared error, consistency, Bayesian formulation, and their loss and potential for marginal and partial correlation screening.

Integrating Surface-related Indicators of Coverage, Distance and Distribution for Quantifying Scan-to-BIM Confidence Level

Abstract. Scan-to-BIM is a widely-used approach to generate Building Information Modelling and by extension Digital Twin models in the architecture, engineering, and construction sector. The resulting models need to be as accurate as possible to ensure subsequent activities that make use of them can do so effectively. Quality assessment of point clouds and occlusion assessment of BIM outputted from Scan-to-BIM has been investigated. However, Scan-to-BIM systems currently do not provide proper metrics as to the confidence the user can have in the quality, in particular geometric quality of the outputted model. This paper addresses this gap by introducing a confidence index, for analysing the reliability of the generated 3D models and thereby quantifying the confidence the user can have in them. Index of confidence is itself derived from three more specific indices: index of coverage estimates the portion of the surface of the modelled element that is explained by the input point cloud. Index of distribution estimates how well the points explaining the modelled surfaces are distributed around the overall object’s surface. Index of distance defines the closeness of the generated element models to the input point cloud. The proposed indices are assessed using three real examples, demonstrating their adequacy.

Cold-water coral mortality under ocean warming is associated with pathogenic bacteria

Cold-water corals form vast reefs that are highly valuable habitats for diverse deep-sea communities. However, as the deep ocean is warming, it is essential to assess the resilience of cold-water corals to future conditions. The effects of elevated temperatures on the cold-water coral Lophelia pertusa (now named Desmophyllum pertusum) from the north-east Atlantic Ocean were experimentally investigated at the holobiont level, the coral host, and its microbiome. We show that at temperature increases of + 3 and + 5 °C, L. pertusa exhibits significant mortality concomitant with changes in its microbiome composition. In addition, a metagenomic approach revealed the presence of gene markers for bacterial virulence factors suggesting that coral death was due to infection by pathogenic bacteria. Interestingly, different coral colonies had different survival rates and, colony-specific microbiome signatures, indicating strong colony-specific variability in their response to warming waters. These results suggest that L. pertusa can only survive a long-term temperature increase of < 3 °C. Therefore, regional variations in deep-sea temperature increase should be considered in future estimates of the global distribution of cold-water corals.

Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia.

Behavioral speech tasks have been widely used to understand the mechanisms of speech motor control in typical speakers as well as in various clinical populations. However, determining which neural functions differ between typical speakers and clinical populations based on behavioral data alone is difficult because multiple mechanisms may lead to the same behavioral differences. For example, individuals with cerebellar ataxia (CA) produce atypically large compensatory responses to pitch perturbations in their auditory feedback, compared to typical speakers, but this pattern could have many explanations. Here, computational modeling techniques were used to address this challenge. Bayesian inference was used to fit a state feedback control (SFC) model of voice fundamental frequency (fo) control to the behavioral pitch perturbation responses of speakers with CA and typical speakers. This fitting process resulted in estimates of posterior likelihood distributions for five model parameters (sensory feedback delays, absolute and relative levels of auditory and somatosensory feedback noise, and controller gain), which were compared between the two groups. Results suggest that the speakers with CA may proportionally weight auditory and somatosensory feedback differently from typical speakers. Specifically, the CA group showed a greater relative sensitivity to auditory feedback than the control group. There were also large group differences in the controller gain parameter, suggesting increased motor output responses to target errors in the CA group. These modeling results generate hypotheses about how CA may affect the speech motor system, which could help guide future empirical investigations in CA. This study also demonstrates the overall proof-of-principle of using this Bayesian inference approach to understand behavioral speech data in terms of interpretable parameters of speech motor control models.

Estimating Vertical Distribution of Total Suspended Matter in Coastal Waters Using Remote-Sensing Approaches

The vertical distribution of the marine total suspended matter (TSM) concentration significantly influences marine material transport, sedimentation processes, and biogeochemical cycles. Traditional field observations are constrained by limited spatial and temporal coverage, necessitating the use of remote-sensing technology to comprehensively understand TSM variations over extensive areas and periods. This study proposes a remote-sensing approach to estimate the vertical distribution of TSM concentrations using MODIS satellite data, with the Bohai Sea and Yellow Sea (BSYS) as a case study. Extensive field measurements across various hydrological conditions and seasons enabled accurate reconstruction of in situ TSM vertical distributions from bio-optical parameters, including the attenuation coefficient, particle backscattering coefficient, particle size, and number concentration, achieving a determination coefficient of 0.90 and a mean absolute percentage error of 26.5%. In situ measurements revealed two distinct TSM vertical profile types (vertically uniform and increasing) and significant variation in TSM profiles in the BSYS. Using surface TSM concentrations, wind speed, and water depth, we developed and validated a remote-sensing approach to classify TSM vertical profile types, achieving an accuracy of 84.3%. Combining this classification with a layer-to-layer regression model, we successfully estimated TSM vertical profiles from MODIS observation. Long-term MODIS product analysis revealed significant spatiotemporal variations in TSM vertical distributions and column-integrated TSM concentrations, particularly in nearshore regions. These findings provide valuable insights for studying marine sedimentation and biological processes and offer a reference for the remote-sensing estimation of the TSM vertical distribution in other marine regions.

MSAO-EDA: A Modified Snow Ablation Optimizer by Hybridizing with Estimation of Distribution Algorithm.

Metaheuristic algorithms provide reliable and effective methods for solving challenging optimization problems. The snow ablation algorithm (SAO) performs favorably as a physics-based metaheuristic algorithm. Nevertheless, SAO has some shortcomings. SAO is overpowered in its exploitation, has difficulty in balancing the proportion of global and local search, and is prone to encountering local optimum traps when confronted with complex problems. To improve the capability of SAO, this paper proposes a modified snow ablation algorithm hybrid distribution estimation algorithm named MSAO-EDA. In this work, a collaborative search framework is proposed where SAO and EDA can be organically integrated together to fully utilize the exploitation capability of SAO and the exploration capability of EDA. Secondly, an offset EDA approach that combines the optimal solution and the agent itself is used to replace SAO's exploration strategy for the purpose of enhancing SAO's exploration capability. Finally, the convergence of SAO is accelerated by selecting the next generation of agents through a greedy strategy. MSAO-EDA is tested on the CEC 2017 and CEC 2022 test suites and compared with EO, RIME, MRFO, CFOA, and four advanced algorithms, AFDBARO, CSOAOA, EOSMA, and JADE. The experimental results show that MSAO-EDA has excellent efficiency in numerical optimization problems and is a highly competitive SAO variant.

Parameter estimation of three-parameter Weibull distribution by hybrid gray genetic algorithm with modified maximum likelihood method with small samples

Parameter estimation of three-parameter Weibull distribution by hybrid gray genetic algorithm with modified maximum likelihood method with small samples

An Analysis of Type-I Generalized Progressive Hybrid Censoring for the One Parameter Logistic-Geometry Lifetime Distribution with Applications

Based on Type-I generalized progressive hybrid censored samples (GPHCSs), the parameter estimate for the unit-half logistic-geometry (UHLG) distribution is investigated in this work. Using maximum likelihood estimation (MLE) and Bayesian estimation, the parameters, reliability, and hazard functions of the UHLG distribution under GPHCSs have been assessed. Likewise, the computation is carried out for the asymptotic confidence intervals (ACIs). Furthermore, two bootstrap CIs, bootstrap-p and bootstrap-t, are mentioned. For symmetric loss functions, like squared error loss (SEL), and asymmetric loss functions, such as linear exponential loss (LL) and general entropy loss (GEL), there are specific Bayesian approximations. The Metropolis–Hastings samplers methodology were used to construct the credible intervals (CRIs). In conclusion, a genuine data set measuring the mortality statistics of a group of male mice with reticulum cell sarcoma is regarded as an application of the methods given.

Detecting market bubbles: A generalized LPPLS neural network model

To enhance bubble detection capabilities, we introduce two significant improvements to the Log-Periodic Power Law Singularity (LPPLS) model: (1) a novel fitting approach, which yields more accurate predictions of critical price distributions within a single sample window; (2) a restructured neural network approach further enhances the estimations of the probability distributions of the critical points across both time and price dimensions, and it can be fine-tuned with real-world data. The simulation and practical applications to typical asset price bubbles in cryptocurrencies, commodities, and equity indices demonstrate that our refined model, the Generalized-LPPLS Neural Network (G-LPPLS-NN), outperforms all other models we examined in terms of predictive accuracy for critical point distributions.

Estimation of vessel link-level travel time distribution: A directed network-driven approach

Accurate vessel travel time estimation is essential for operational efficiency and route optimization. Despite the prevalent use of the automatic identification system (AIS) in garnering multifaceted real-time data, ambiguities and inaccuracies in travel time predictions persist. It leads to planning uncertainties, inefficient resource allocations, and heightened operational costs in maritime logistics. To addresses these issues, this paper proposed a nuanced method to enhance the precision and reliability of vessel travel time estimations. Firstly, a directed maritime network is constructed by extracting essential information from AIS-based historical vessel trajectories. This lays the foundation for the subsequent analytical processes. Secondly, the non-parametric kernel density estimation (KDE) is applied to this constructed network, enabling the estimation of vessel travel time distributions across various network links. The non-parametric KDE is used in combination with AIS data, which improves the specificity and accuracy of the travel time estimations at the link level. Finally, this paper employs cellular automata (CA) simulations to validate the accuracy of the KDE-based estimations. Comparison between the simulation results and real-world data reveals a high degree of accuracy in the proposed method, confirming its applicability and effectiveness in estimating vessel travel times.

In Silico SwissADME Analysis of Antibacterial NHC–Silver Acetates and Halides Complexes

This study investigates the antibacterial N-heterocyclic carbene (NHC)–silver complexes using the SwissADME platform, a web-based tool developed by the Swiss Institute of Bioinformatics (SIB). NHCs, particularly their silver complexes, have gained significant interest in medicinal chemistry for their potential as antibacterial and anticancer agents. The effectiveness of these complexes is closely linked to their structure, including factors like lipophilicity, which enhance their ability to penetrate bacterial cells and sustain the release of active silver ions. SwissADME provides computational estimates of pharmacokinetic properties, including absorption, distribution, metabolism, and excretion (ADME) characteristics, as well as drug-likeness and toxicity assessments. By evaluating parameters like molecular weight, topological polar surface area, lipophilicity (LogP), and water solubility, SwissADME offers insights into the drug-like potential of compounds. This study is inspired by a comprehensive review of antibacterial NHC–silver complexes published from 2006 to 2023, which identified superior structures with notable biological activity. The primary aim is to determine whether these active complexes exhibit distinct SwissADME parameters compared to others, providing a deeper understanding of the factors that influence their biological efficacy and aiding in the identification of promising drug candidates. Finally, experimental stabilities of exemplary complexes were confronted with absolute LUMO values derived from DFT calculations.

On fractional moment estimation from polynomial chaos expansion

Fractional statistical moments are utilized for various tasks of uncertainty quantification, including the estimation of probability distributions. However, an estimation of fractional statistical moments of costly mathematical models by statistical sampling is challenging since it is typically not possible to create a large experimental design due to limitations in computing capacity. This paper presents a novel approach for the analytical estimation of fractional moments, directly from polynomial chaos expansions. Specifically, the first four statistical moments obtained from the deterministic coefficients of polynomial chaos expansion are used for an estimation of arbitrary fractional moments via Hölder’s inequality. The proposed approach is utilized for an estimation of statistical moments and probability distributions in four numerical examples of increasing complexity. Obtained results show that the proposed approach achieves a superior performance in estimating the distribution of the response, in comparison to a standard Latin hypercube sampling in the presented examples.

MEFDPN: Mixture exponential family distribution posterior networks for evaluating data uncertainty

The computation of uncertainty are crucial for developing a reliable machine learning model. The natural posterior network (NatPN) provides uncertainty estimation for any single exponential family distribution, but real-world data is often complex. Therefore, we introduce a mixture exponential family posterior network (MEFDPN), which extends the prior distribution to a mixture of exponential family distributions, aiming to fit complex distributions that better represent real data. During network training, MEFDPN independently updates the posterior Bayesian estimates for each prior distribution, and the weights of these distributions are updated based on the forward propagation results. Furthermore, MEFDPN calculates two types of uncertainty (aleatoric and epistemic) and combines them using entropy weighting to obtain a comprehensive confidence measure for each data point. Theoretically, MEFDPN achieves higher prediction accuracy, and experimental results demonstrate its capability to compute high-quality data comprehensive confidence. Moreover, it shows encouraging accuracy in Out-of-Distribution(OOD) detection and validation experiments. Finally, we apply MEFDPN to a materials dataset, efficiently filtering out OOD data. This results in a significant enhancement of prediction accuracy for machine learning models. Specifically, removing only 5% of outlier data leads to a 2%–5% improvement in accuracy.

Characteristics and Consequences of Violent Victimization in Sexual and Gender Minority Communities: An Analysis of the 2017-2021 National Crime Victimization Survey.

Purpose: This article investigates rates of violent victimization, subsequent help-seeking, and health-related consequences within sexual and gender minority (SGM) communities. Methods: Aggregate data from the 2017-2021 National Crime Victimization Survey were examined to determine nationally representative estimates of rates and distributions of violent victimization, help-seeking, and socioemotional consequences within those 16 years of age and older. Due to sample size, most analyses aggregated sexual orientation and gender identity to allow comparison of SGM persons to non-SGM persons and examine differences within the SGM population. Results: Persons who identified as lesbian, gay, or bisexual experienced violent victimization at rates two to six times higher than straight persons. Transgender persons were victimized more than three times as often than cisgender persons. SGM persons experienced higher rates of all types of violent victimization than non-SGM persons regardless of victim-offender relationship. There were differences by victim demographic characteristics, including sex, race and Hispanic origin, age, marital status, and household income. A higher proportion of SGM victims reported only problems with work/school or problems both at work/school and with family/friends. Finally, higher proportions of SGM victims reported socioemotional consequences when they were female, older, or experienced serious violent crime. Conclusion: The findings in this study continue to highlight high levels of violence experienced by SGM persons and disproportionate socioemotional consequences. There is an evident need to develop targeted interventions and provide services to address the consequences of victimization among this population. The analyses demonstrate the necessity of continued research to better understand the impact of violence on SGM communities.