Growth trends clustering: A novel method for detecting forest disturbances and extracting climate signals in tree rings.

Tree-ring widths contain valuable historical information related to both forest disturbances and climate variability and changes within forests. However, current methods are still unable to accurately distinguish between disturbances and climate signals in tree rings, especially in the case of climate anomalies. To address this issue, we developed a novel method, called Growth Trends Clustering (GTC) that uses the distribution characteristics of tree-ring widths within a stand to distinguish the effects of climate and other forest disturbances. GTC employed a Gaussian mixture model to fit the probability density distribution of annual ring-width index (RWI) in a stand. Discriminative criteria were established to cluster diverse sub-distributions from the Gaussian mixture model into categories of growth release, suppression, or normal trends. This approach allowed us to identify the occurrence, duration, and severity of forest disturbances based on percentage changes in the growth release or suppression categories of trees. And the effect of climate on tree growth was assessed according to the mean statistics of the growth normal categories. Using common forest disturbances such as defoliating insects and thinning as examples, we validated our method using tree-ring collections from six sites in British Columbia and Quebec, Canada. We found that the GTC method was superior to traditional time-series analysis methods (e.g., Radial Growth Averaging, Boundary Line, Absolute Increase, and Curve Intervention Detection) for detecting past forest disturbances and was able to significantly enhance climate signals. In summary, the GTC method presented in this study introduces a novel statistical approach for accurately distinguishing between forest disturbances and climate signals in tree rings. This is particularly important for understanding forest disturbance regimes under climate change and for developing future disturbance mitigation strategies.

Psychometric benefits of self-chosen rating scales over given rating scales.

Rating scales are susceptible to response styles that undermine the scale quality. Optimizing a rating scale can tailor it to individuals' cognitive abilities, thereby preventing the occurrence of response styles related to a suboptimal response format. However, the discrimination ability of individuals in a sample may vary, suggesting that different rating scales may be appropriate for different individuals. This study aims to examine (1) whether response styles can be avoided when individuals are allowed to choose a rating scale and (2) whether the psychometric properties of self-chosen rating scales improve compared to given rating scales. To address these objectives, data from the flourishing scale were used as an illustrative example. MTurk workers from Amazon's Mechanical Turk platform (N = 7042) completed an eight-item flourishing scale twice: (1) using a randomly assigned four-, six-, or 11-point rating scale, and (2) using a self-chosen rating scale. Applying the restrictive mixed generalized partial credit model (rmGPCM) allowed examination of category use across the conditions. Correlations with external variables were calculated to assess the effects of the rating scales on criterion validity. The results revealed consistent use of self-chosen rating scales, with approximately equal proportions of the three response styles. Ordinary response behavior was observed in 55-58% of individuals, which was an increase of 12-15% compared to assigned rating scales. The self-chosen rating scales also exhibited superior psychometric properties. The implications of these findings are discussed.

An extended multiplicative error model of allometry: Incorporating systematic components, non-normal distributions, and piecewise heteroscedasticity.

Allometry refers to the relationship between the size of a trait and that of the whole body of an organism. Pioneering observations by Otto Snell and further elucidation by D'Arcy Thompson set the stage for its integration into Huxley's explanation of constant relative growth that epitomizes through the formula of simple allometry. The traditional method to identify such a model conforms to a regression protocol fitted in the direct scales of data. It involves Huxley's formula-systematic part and a lognormally distributed multiplicative error term. In many instances of allometric examination, the predictive strength of this paradigm is unsuitable. Established approaches to improve fit enhance the complexity of the systematic relationship while keeping the go-along normality-borne error. These extensions followed Huxley's idea that considering a biphasic allometric pattern could be necessary. However, for present data composing 10 410 pairs of measurements of individual eelgrass leaf dry weight and area, a fit relying on a biphasic systematic term and multiplicative lognormal errors barely improved correspondence measure values while maintaining a heavy tails problem. Moreover, the biphasic form and multiplicative-lognormal-mixture errors did not provide complete fit dependability either. However, updating the outline of such an error term to allow heteroscedasticity to occur in a piecewise-like mode finally produced overall fit consistency. Our results demonstrate that when attempting to achieve fit quality improvement in a Huxley's model-based multiplicative error scheme, allowing for a complex allometry form for the systematic part, a non-normal distribution-driven error term and a composite of uneven patterns to describe the heteroscedastic outline could be essential.

A novel dynamic outlier-robust Kalman filter with Moving Horizon Estimation.

The existence of dynamic outliers poses a significant challenge to the Kalman filter (KF). In addressing this challenge, this paper presents an innovative solution: Firstly, by analyzing a period of measurement information to more accurately identify state and measurement dynamic outliers, the system's capacity to adapt to dynamic changes is significantly improved. Next, noise is modeled as a Gaussian-Student's t mixture distribution (GSTM), with mixed model parameters inferred using the variational Bayesian (VB) method based on measurement information, cleverly integrated into the Moving Horizon Estimation (MHE) framework, thus enhancing the flexibility and accuracy of the noise model. Lastly, the optimal window size was identified through simulation experiment analysis to further increase the estimation accuracy. Simulation results demonstrate that the proposed filter exhibits stronger robustness in resisting dynamic outliers compared to existing filters.

Corrigendum: Degenerate Beta autoregressive model for proportion time-series with zeros or ones: an application to antimicrobial resistance rate using R shiny app.

[This corrects the article DOI: 10.3389/fpubh.2022.969777.].

SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials.

Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a noninformative prior. However, prespecifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which is often unknown beforehand, posing a significant obstacle to the application and acceptance of mixture priors. To address this challenge, we introduce self-adapting mixture (SAM) priors that determine the mixing weight using likelihood ratio test statistics or Bayes factors. SAM priors are data-driven and self-adapting, favoring the informative (noninformative) prior component when there is little (substantial) evidence of prior-data conflict. Consequently, SAM priors achieve dynamic information borrowing. We demonstrate that SAM priors exhibit desirable properties in both finite and large samples and achieve information-borrowing consistency. Moreover, SAM priors are easy to compute, data-driven, and calibration-free, mitigating the risk of data dredging. Numerical studies show that SAM priors outperform existing methods in adopting prior-data conflicts effectively. We developed R package "SAMprior" and web application that are freely available at CRAN and www.trialdesign.org to facilitate the use of SAM priors.

A Robust Method for Detecting Item Misfit in Large-Scale Assessments.

Viable methods for the identification of item misfit or Differential Item Functioning (DIF) are central to scale construction and sound measurement. Many approaches rely on the derivation of a limiting distribution under the assumption that a certain model fits the data perfectly. Typical DIF assumptions such as the monotonicity and population independence of item functions are present even in classical test theory but are more explicitly stated when using item response theory or other latent variable models for the assessment of item fit. The work presented here provides a robust approach for DIF detection that does not assume perfect model data fit, but rather uses Tukey's concept of contaminated distributions. The approach uses robust outlier detection to flag items for which adequate model data fit cannot be established.

Shifted Mixture Model Using Weibull, Lognormal, and Gamma Distributions.

The objective of this investigation is to provide framework to construct a threefold mixture model and its shifted version using Weibull, lognormal, and gamma distributions. The proposed models are examined by establishing the statistical and reliability indices. The parameter estimation using the maximum likelihood estimation method (MLE) and expectation-maximization has been proposed. The usefulness of the shifted mixture models by fitting them into the actual data set has revealed. The goodness-of-fit tests are used to compare the mixture models for the real-life data. Based on statistical testing, it is established that for small data set, shifted mixture model is the best fitted model in comparison with other single and mixed mixture distributions.

White matter microstructure is associated with the precision of visual working memory.

Visual working memory is critical for goal-directed behavior as it maintains continuity between previous and current visual input. Functional neuroimaging studies have shown that visual working memory relies on communication between distributed brain regions, which implies an important role for long-range white matter connections in visual working memory performance. Here, we characterized the relationship between the microstructure of white matter association tracts and the precision of visual working memory representations. To that purpose, we devised a delayed estimation task which required participants to reproduce visual features along a continuous scale. A sample of 80 healthy adults performed the task and underwent diffusion-weighted MRI. We applied mixture distribution modelling to quantify the precision of working memory representations, swap errors, and guess rates, all of which contribute to observed responses. Latent components of microstructural properties in sets of anatomical tracts were identified by principal component analysis. We found an interdependency between fibre coherence in the bilateral superior longitudinal fasciculus (SLF) I, SLF II, and SLF III, on one hand, and the bilateral inferior fronto-occipital fasciculus (IFOF), on the other, in mediating the precision of visual working memory in a functionally specific manner. We also found that individual differences in axonal density in a network comprising the bilateral inferior longitudinal fasciculus (ILF) and SLF III and right SLF II, in combination with a supporting network located elsewhere in the brain, form a common system for visual working memory to modulate response precision, swap errors, and random guess rates.

Blowin' in the wind: Dispersal of Glossy Ibis Plegadis falcinellus in the West Mediterranean basin.

The movement of organisms is a central process in ecology and evolution, and understanding the selective forces shaping the spatial structure of populations is essential to conservation. Known as a trans-Saharan migrant capable of long-distance flights, the Glossy Ibis Plegadis falcinellus' dispersal remains poorly known. We started a ringing scheme in 2008, the first of its kind in North Africa, and ringed 1121 fledglings over 10 years, of which 265 (23.6%) were resighted. Circular statistics and finite mixture models of natal dispersal indicated: (1) a strong West/Northwest-East/Southeast flight orientation; (2) Glossy Ibis colonies from North Africa and Southern Europe (particularly on the Iberian Peninsula) are closely linked through partial exchanges of juvenile and immature birds; (3) unlike birds from Eastern Europe, North African Glossy Ibis disperse to but do not seem to undergo regular round-trip migration to the Sahel; (4) young adults (>2-years-old) have a higher probability of dispersing further than individuals in their first calendar year (<1-year-old); and (5) dispersal distance is not influenced by sex or morphometric traits. Together, these results enhance our knowledge of the dispersal and metapopulation dynamics of Glossy Ibis, revealing large-scale connectivity between the Iberian Peninsula and Algeria, likely driven by the spatial heterogeneity of the landscape in these two regions and the prevailing winds in the Western Mediterranean.

scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data.

The progress of single-cell RNA sequencing (scRNA-seq) has led to a large number of scRNA-seq data, which are widely used in biomedical research. The noise in the raw data and tens of thousands of genes pose a challenge to capture the real structure and effective information of scRNA-seq data. Most of the existing single-cell analysis methods assume that the low-dimensional embedding of the raw data belongs to a Gaussian distribution or a low-dimensional nonlinear space without any prior information, which limits the flexibility and controllability of the model to a great extent. In addition, many existing methods need high computational cost, which makes them difficult to be used to deal with large-scale datasets. Here, we design and develop a depth generation model named Gaussian mixture adversarial autoencoders (scGMAAE), assuming that the low-dimensional embedding of different types of cells follows different Gaussian distributions, integrating Bayesian variational inference and adversarial training, as to give the interpretable latent representation of complex data and discover the statistical distribution of different types of cells. The scGMAAE is provided with good controllability, interpretability and scalability. Therefore, it can process large-scale datasets in a short time and give competitive results. scGMAAE outperforms existing methods in several ways, including dimensionality reduction visualization, cell clustering, differential expression analysis and batch effect removal. Importantly, compared with most deep learning methods, scGMAAE requires less iterations to generate the best results.

Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks.

Predicting the number of outstanding claims (IBNR) is a central problem in actuarial loss reserving. Classical approaches like the Chain Ladder method rely on aggregating the available data in form of loss triangles, thereby wasting potentially useful additional claims information. A new approach based on a micro-level model for reporting delays involving neural networks is proposed. It is shown by extensive simulation experiments and an application to a large-scale real data set involving motor legal insurance claims that the new approach provides more accurate predictions in case of non-homogeneous portfolios. Supplementary Information: The online version contains supplementary material available at 10.1007/s13385-022-00314-4.

Development of an assessment framework for the proposed Multi-Scalar Seasonally Amalgamated Regional Standardized Precipitation Evapotranspiration Index (MSARSPEI) for regional drought classifications in global warming context.

Drought hazard is one of the main consequences of global warming and climate change. Unlike other natural disasters, drought has complex climatic features. Therefore, accurate drought monitoring is a challenging task. This paper proposes a framework for assessing drought classifications at the regional level. The proposed framework provides a new drought monitoring indicator called Multi-Scalar Seasonally Amalgamated Regional Standardized Precipitation Evapotranspiration Index (MSARSPEI). MSARSPEI is an amalgam of the Standardized Precipitation Evapotranspiration (SPEI) (Vicente-Serrano et al., 2010) and Regionally Improved Weighted Standardized Drought Index (RIWSDI) (Jiang et al., 2020). In the proposed framework, the Boruta algorithm of feature selection is configured to ensemble monthly time series data of evaporation in various meteorological stations located in specific regions. Further, the framework suggests the standardization of the Cumulative Distribution Function (CDF) of K-Component Gaussian (K-CG) mixture distribution function for obtaining MSARSPEI data. The application of the proposed framework is based on seven different regions of Pakistan. For comparative analysis, this paper compared the performance of MSARSPE with SPEI using Pearson correlation. Outcomes associated with this research show that the proposed regional drought index has a strong correlation with the competing indicator in various time scales. In addition, the study assessed the spatial extent of various drought classifications under MSARSPEI. In summation, this research concludes that the choice of the MSARSPEI is rationally valid and more appropriate for the regional assessment of drought under the global warming scenario.

Mixture survival trees for cancer risk classification.

In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.

A mixture distribution approach for assessing genetic impact from twin study.

It is challenging to evaluate the genetic impacts on a biologic feature and separate them from environmental impacts. This is usually achieved through twin studies by assessing the collective genetic impact defined by the differential correlation in monozygotic twins vs dizygotic twins. Since the underlying order in a twin, determined by latent genetic factors, is unknown, the observed twin data are unordered. Conventional methods for correlation are not appropriate. To handle the missing order, we model twin data by a mixture bivariate distribution and estimate under two likelihood functions: the likelihood over the monozygotic and dizygotic twins separately, and the likelihood over the two twin types combined. Both likelihood estimators are consistent. More importantly, the combined likelihood overcomes the drawback of mixture distribution estimation, namely, the slow convergence. It yields correlation coefficient estimator of root-n consistency and allows effective statistical inference on the collective genetic impact. The method is demonstrated by a twin study on immune traits.

Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app.

Background: Antimicrobial resistance has emerged as one of the foremost public health troubles of the 21st century. This has ended in a public health disaster of the global situation, which threatens the exercise of present-day remedy. There is an urgent requirement for a cost-effective strategy to reduce antimicrobial resistance. Infectious disease control researchers most often analyze and predict antimicrobial resistance rate data that includes zeros or ones. Commonly used time-series analysis such as autoregressive moving average model is inappropriate for such data and may arrive at biased results. Objective: This study aims to propose a time-series model for continuous rates or proportions when the interval of series includes zeros or ones and compares the model with existing models. Data: The Escherichia coli, isolated from blood cultures showing variable susceptibility results to different antimicrobial agents, has been obtained from a clinical microbiology laboratory of a tertiary care hospital, Udupi district, Karnataka, during the years between 2011 and 2019. Methodology: We proposed a Degenerate Beta Autoregressive model which is a mixture of continuous and discrete distributions with probability mass at zero or one. The proposed model includes autoregressive terms along with explanatory variables. The estimation is done using maximum likelihood with a non-linear optimization algorithm. An R shiny app has been provided for the same. Results: The proposed Degenerate Beta Autoregressive model performed well compared to the existing autoregressive moving average models. The forecasted antimicrobial resistance rate has been obtained for the next 6 months. Conclusion: The findings of this article could be beneficial to the infectious disease researchers to use an appropriate time-series model to forecast the resistance rate for the future and to have better or advance public health policies to control the rise in resistance rate.

A New Variational Bayesian-Based Kalman Filter with Unknown Time-Varying Measurement Loss Probability and Non-Stationary Heavy-Tailed Measurement Noise.

In this paper, a new variational Bayesian-based Kalman filter (KF) is presented to solve the filtering problem for a linear system with unknown time-varying measurement loss probability (UTVMLP) and non-stationary heavy-tailed measurement noise (NSHTMN). Firstly, the NSHTMN was modelled as a Gaussian-Student's t-mixture distribution via employing a Bernoulli random variable (BM). Secondly, by utilizing another Bernoulli random variable (BL), the form of the likelihood function consisting of two mixture distributions was converted from a weight sum to an exponential product and a new hierarchical Gaussian state-space model was therefore established. Finally, the system state vector, BM, BL, the intermediate random variables, the mixing probability, and the UTVMLP were jointly inferred by employing the variational Bayesian technique. Simulation results revealed that in the scenario of NSHTMN, the proposed filter had a better performance than current algorithms and further improved the estimation accuracy of UTVMLP.

A Bayesian Mixture Modelling of Stop Signal Reaction Time Distributions: The Second Contextual Solution for the Problem of Aftereffects of Inhibition on SSRT Estimations.

The distribution of single Stop Signal Reaction Times (SSRT) in the stop signal task (SST) has been modelled with two general methods: a nonparametric method by Hans Colonius (1990) and a Bayesian parametric method by Dora Matzke, Gordon Logan and colleagues (2013). These methods assume an equal impact of the preceding trial type (go/stop) in the SST trials on the SSRT distributional estimation without addressing the relaxed assumption. This study presents the required model by considering a two-state mixture model for the SSRT distribution. It then compares the Bayesian parametric single SSRT and mixture SSRT distributions in the usual stochastic order at the individual and the population level under ex-Gaussian (ExG) distributional format. It shows that compared to a single SSRT distribution, the mixture SSRT distribution is more varied, more positively skewed, more leptokurtic and larger in stochastic order. The size of the results' disparities also depends on the choice of weights in the mixture SSRT distribution. This study confirms that mixture SSRT indices as a constant or distribution are significantly larger than their single SSRT counterparts in the related order. This result offers a vital improvement in the SSRT estimations.

A combination of finite mixture distribution model with geo-statistical models to study spatial patterns and hazardous areas of heavy metals in cropland soils of the Guanzhong Plain, Northwest China.

An extensive cropland soil investigation was conducted to determine the pollution thresholds and hazardous zones of heavy metals (HMs) in the Guanzhong Plain, by using an integrated approach that combines finite mixture distribution model (FMDM) and geo-statistical analysis. FMDM results demonstrated that Pb, Cr, Ni, and Cu were fitted by binary mixture distributions representing the background and moderate pollution distributions, and Zn was fitted by a triple mixture distribution representing the background, moderate and high contamination distributions. The moderate pollution thresholds of Pb, Cr, Ni, Zn and Cu calculated by FMDM were 29.75, 80.15, 38.60, 81.48 and 27.10 mg kg-1, whereas the cutoff value of Zn high contamination was 97.49 mg kg-1. The moderately polluted thresholds of all five HMs were higher than their background values in the study area, and lower than the corresponding national standards. The indicator kriging simulation showed Pb, Cr, Ni, Zn had <0.1%, 2.6%, <0.1%, 2.9% of total areas exceed contamination cutoff values, whereas the hazardous area of Cu was contiguous, and covered 17.3% of the total area. Overall, 17.5% of the total area surpassed the moderate contamination threshold. The pollution hot spots and hazardous zones of soil HMs were located in the southern part of the Guanzhong Plain, where population and industrial activities are centralized, indicating that anthropogenic activities played a critical role in HMs accumulation in high-risk regions. The combination of geo-statistical and FMDM delineate the thresholds and hazardous area for HMs pollution reliably, and facilitate the improvement of soil environmental management.

Inferring latent heterogeneity using many feature variables supervised by survival outcome.

In cancer studies, it is important to understand disease heterogeneity among patients so that precision medicine can particularly target high-risk patients at the right time. Many feature variables such as demographic variables and biomarkers, combined with a patient's survival outcome, can be used to infer such latent heterogeneity. In this work, we propose a mixture model to model each patient's latent survival pattern, where the mixing probabilities for latent groups are modeled through a multinomial distribution. The Bayesian information criterion is used for selecting the number of latent groups. Furthermore, we incorporate variable selection with the adaptive lasso into inference so that only a few feature variables will be selected to characterize the latent heterogeneity. We show that our adaptive lasso estimator has oracle properties when the number of parameters diverges with the sample size. The finite sample performance is evaluated by the simulation study, and the proposed method is illustrated by two datasets.