Information Criterion Model Selection Research Articles

A sticky Poisson Hidden Markov Model for spike data.

Fitting a hidden Markov Model (HMM) to neural data is a powerful method to segment a spatiotemporal stream of neural activity into sequences of discrete hidden states. Application of HMM has allowed to uncover hidden states and signatures of neural dynamics that seem relevant for sensory and cognitive processes. This has been accomplished especially in datasets comprising ensembles of simultaneously recorded cortical spike trains. However, the HMM analysis of spike data is involved and requires a careful handling of model selection. Two main issues are: (i) the cross-validated likelihood function typically increases with the number of hidden states; (ii) decoding the data with an HMM can lead to very rapid state switching due to fast oscillations in state probabilities. The first problem is related to the phenomenon of over-segmentation and leads to overfitting. The second problem is at odds with the empirical fact that hidden states in cortex tend to last from hundred of milliseconds to seconds. Here, we show that we can alleviate both problems by regularizing a Poisson-HMM during training so as to enforce large self-transition probabilities. We call this algorithm the 'sticky Poisson-HMM' (sPHMM). When used to-gether with the Bayesian Information Criterion for model selection, the sPHMM successfully eliminates rapid state switching, outperforming an alternative strategy based on an HMM with a large prior on the self-transition probabilities. The sPHMM also captures the ground truth in surrogate datasets built to resemble the statistical properties of the experimental data.

Total-Body Dynamic Imaging and Kinetic Modeling of [18F]F-AraG in Healthy Individuals and a Non-Small Cell Lung Cancer Patient Undergoing Anti-PD-1 Immunotherapy.

Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune system's capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. 18F-arabinosyl guanine ([18F]F-AraG) is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by noninvasive quantification of immune cell activity within the tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of [18F]F-AraG as a potential quantitative biomarker for immune response evaluation. Methods: The study consisted of 90-min total-body dynamic scans of 4 healthy subjects and 1 non-small cell lung cancer patient who was scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection was used to analyze tracer kinetics in various organs. Additionally, 7 subregions of the primary lung tumor and 4 mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess the reliability of kinetic parameter estimation. Correlations of the SUVmean, the tissue-to-blood SUV ratio (SUVR), and the Logan plot slope (K Logan) with the total volume of distribution (V T) were calculated to identify potential surrogates for kinetic modeling. Results: Strong correlations were observed between K Logan and SUVR with V T, suggesting that they can be used as promising surrogates for V T, especially in organs with a low blood-volume fraction. Moreover, practical identifiability analysis suggested that dynamic [18F]F-AraG PET scans could potentially be shortened to 60 min, while maintaining quantification accuracy for all organs of interest. The study suggests that although [18F]F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response after therapy. Although SUVmean showed variable changes in different subregions of the tumor after therapy, the SUVR, K Logan, and V T showed consistent increasing trends in all analyzed subregions of the tumor with high practical identifiability. Conclusion: Our findings highlight the promise of [18F]F-AraG dynamic imaging as a noninvasive biomarker for quantifying the immune response to immunotherapy in cancer patients. Promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.

A Generalized Kinetic Model of Fractional Order Transport Dynamics with Transit Time Heterogeneity in Microvascular Space.

The aim of this study is to develop and validate a unifying kinetic model for microvascular transport by introducing an impulse response function that incorporates essential physiological parameters and integrates key features of existing models. This new methodology combines a one-compartment model of fractional order with a model that uses the gamma distribution to describe the distribution of capillary transit times. Central to this model are two primary parameters: [Formula: see text], representing the kurtosis of residue times, and [Formula: see text], signifying the width of the distribution of capillary transit times within a tissue voxel. To validate this proposed model, data from dynamic contrast-enhanced magnetic resonance imaging (DCI-MRI) were employed and the findings were compared with three existing models. Using the Akaike information criterion for model selection, the results demonstrate that the integrative model, especially at elevated blood flow rates, frequently offers superior fits in comparison to constrained models.

Advanced HIV disease and its predictors among newly diagnosed PLHIV in the Gedeo zone, southern Ethiopia.

Globally, HIV infection remains a leading cause of morbidity and mortality. Despite reducing new infections, the global response to advanced HIV disease (AHD) remains ineffective, leaving HIV epidemics a significant public health threat worldwide. In Ethiopia, evidence regarding AHD is scarce. Therefore, this study aimed to assess the prevalence and predictors of AHD among newly diagnosed people living with HIV (PLHIV) initiating antiretroviral therapy in the Gedeo zone, southern Ethiopia. A facility-based cross-sectional study was conducted from May 29, 2023, to February 06, 2024, at health facilities providing HIV care in the Gedeo zone, southern Ethiopia. A total of 427 PLHIV-initiating antiretroviral therapy (ART) were recruited for the study. The data were collected through face-to-face interviews and record reviews using KoboCollect version 2.4 and analyzed using R version 4.3.3. The Akaike information criterion (AIC) model selection was used to evaluate and choose the best-fitting model to describe the relationship between AHD and predictors. Finally, variables with a p-value less than 0.05 were considered independent predictors in the multivariable regression analysis. The study participants' mean (±SD) age was 31.3 (±8.7) years. The overall prevalence of AHD among newly diagnosed PLHIV-initiating ART was 34.4% (95% CI: 29.8%, 39.1%). Rural residence (AOR = 3.48, 95% CI: 2.24, 5.47), alcohol consumption (AOR = 2.48, 95% CI: 1.59, 3.90), and being identified through community-based index case testing (ICT) (AOR = 0.26, 95% CI: 0.13, 0.51) were found to be independent predictors of AHD. The prevalence of AHD among newly diagnosed individuals initiating ART was high. PLHIV who consume alcohol should receive detailed counseling on how it can negatively impact their progress with antiretroviral treatment. HIV testing should be enhanced in rural communities by strengthening community health campaigns. Furthermore, community-based index case testing should be strengthened for early identification of PLHIV.

Ontogenetic and static scaling of antler mass in White-tailed Deer (Odocoileus virginianus)

Abstract Antlers are a costly trait that require skeletal reserves to grow to a large size. Thus, insight into variation in antler size requires understanding the connection between antler and body size, which can be summarized through ontogenetic and static scaling relationships. Both types of scaling relationships are also needed to examine the influence of factors besides body mass on antler growth at different ages and whether the effects of these factors diminish with age. Dietary and maternal effects might decline with age as individuals attempt to mitigate size deficits. We examined the potential effects of diet and maternal attributes of dam (mother) age at birth and litter size on body–antler mass relationships throughout ontogeny and at discrete ages. Data on age, maternal characteristics, body mass, and antler mass were gathered from captive, pen-raised White-tailed Deer (n = 168) that consumed either a low-energy (1.77 kcal/g) or standard-energy diet (2.65 kcal/g) from the time they were weaned until they perished by 5.5 years of age. Both types of scaling relationships were estimated with linear mixed-effects models to account for repeated measurements of males, dams, and sires. Diet affected ontogenetic scaling relationships. Males eating the low-energy diet had faster antler growth when young and lighter in body mass than when they were older and heavier—whereas males fed the standard-energy diet had a similar rate of antler growth across the range of body masses. A Bayesian Information Criterion model selection analysis indicated that diet and litter size (singleton, multiple births), but not dam age at birth influenced static scaling relationships. Static scalar coefficients up to 3.5 years of age were positively allometric (>1.0), but isometric (1.0) in 4.5- and 5.5-year-old males. Furthermore, diet and litter size influenced both intercepts and slopes in only the youngest males (1.5 years) indicating that dietary and maternal effects can diminish with age. Body–antler mass relationships are complex. Young males that are small because of maternal effects, for example, might still be able to mitigate smaller body and antler sizes at older ages.

A RELUCTANT ADDITIVE MODEL FRAMEWORK FOR INTERPRETABLE NONLINEAR INDIVIDUALIZED TREATMENT RULES.

Individualized treatment rules (ITRs) for treatment recommendation is an important topic for precision medicine as not all beneficial treatments work well for all individuals. Interpretability is a desirable property of ITRs, as it helps practitioners make sense of treatment decisions, yet there is a need for ITRs to be flexible to effectively model complex biomedical data for treatment decision making. Many ITR approaches either focus on linear ITRs, which may perform poorly when true optimal ITRs are nonlinear, or black-box nonlinear ITRs, which may be hard to interpret and can be overly complex. This dilemma indicates a tension between interpretability and accuracy of treatment decisions. Here we propose an additive model-based nonlinear ITR learning method that balances interpretability and flexibility of the ITR. Our approach aims to strike this balance by allowing both linear and nonlinear terms of the covariates in the final ITR. Our approach is parsimonious in that the nonlinear term is included in the final ITR only when it substantially improves the ITR performance. To prevent overfitting, we combine crossfitting and a specialized information criterion for model selection. Through extensive simulations we show that our methods are data-adaptive to the degree of nonlinearity and can favorably balance ITR interpretability and flexibility. We further demonstrate the robust performance of our methods with an application to a cancer drug sensitive study.

Comparing estimation methods for psychometric networks with ordinal data.

Ordinal data are extremely common in psychological research, with variables often assessed using Likert-type scales that take on only a few values. At the same time, researchers are increasingly fitting network models to ordinal item-level data. Yet very little work has evaluated how network estimation techniques perform when data are ordinal. We use a Monte Carlo simulation to evaluate and compare the performance of three estimation methods applied to either Pearson or polychoric correlations: extended Bayesian information criterion graphical lasso with regularized edge estimates ("EBIC"), Bayesian information criterion model selection with partial correlation edge estimates ("BIC"), and multiple regression with p-value-based edge selection and partial correlation edge estimates ("MR"). We vary the number and distribution of thresholds, distribution of the underlying continuous data, sample size, model size, and network density, and we evaluate results in terms of model structure (sensitivity and false positive rate) and edge weight bias. Our results show that the effect of treating the data as ordinal versus continuous depends primarily on the number of levels in the data, and that estimation performance was affected by the sample size, the shape of the underlying distribution, and the symmetry of underlying thresholds. Furthermore, which estimation method is recommended depends on the research goals: MR methods tended to maximize sensitivity of edge detection, BIC approaches minimized false positives, and either one of these produced accurate edge weight estimates in sufficiently large samples. We identify some particularly difficult combinations of conditions for which no method produces stable results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Empirical bias-reducing adjustments to estimating functions

Abstract We develop a novel, general framework for reduced-bias M-estimation from asymptotically unbiased estimating functions. The framework relies on an empirical approximation of the bias by a function of derivatives of estimating function contributions. Reduced-bias M-estimation operates either implicitly, solving empirically adjusted estimating equations, or explicitly, subtracting the estimated bias from the original M-estimates, and applies to partially or fully specified models with likelihoods or surrogate objectives. Automatic differentiation can abstract away the algebra required to implement reduced-bias M-estimation. As a result, the bias-reduction methods, we introduce have broader applicability, straightforward implementation, and less algebraic or computational effort than other established bias-reduction methods that require resampling or expectations of products of log-likelihood derivatives. If M-estimation is by maximising an objective, then there always exists a bias-reducing penalised objective. That penalised objective relates to information criteria for model selection and can be enhanced with plug-in penalties to deliver reduced-bias M-estimates with extra properties, like finiteness for categorical data models. Inferential procedures and model selection procedures for M-estimators apply unaltered with the reduced-bias M-estimates. We demonstrate and assess the properties of reduced-bias M-estimation in well-used, prominent modelling settings of varying complexity.

Quantum information criteria for model selection in quantum state estimation

Quantum state estimation (or state tomography) is an indispensable task in quantum information processing. Because full state tomography that determines all elements of the density matrix is computationally demanding, one usually takes the strategy of assuming a certain model of quantum states and identifying the model parameters. However, it is difficult to make a valid assumption given little prior knowledge on a quantum state of interest, and thus we need a reasonable model selection method for quantum state estimation. Actually, in the classical statistical estimation theory, several types of information criteria have been established and widely used in practice for appropriately choosing a classical statistical model. In this study, we propose quantum information criteria for evaluating the quality of the estimated quantum state in terms of the quantum relative entropy, which is a natural quantum analogue of the classical information criterion defined in terms of Kullback–Leibler divergence. In particular, we derive two quantum information criteria depending on the type of an estimator for the quantum relative entropy; one uses the log-likelihood and the other uses the classical shadow. The general role of information criteria is to predict the performance of an estimated model for unseen data, although it is a function of only sampled data; this generalization capability of the proposed quantum information criteria is evaluated in numerical simulations.

Model-independent cosmological insights from three newly reconstructed deceleration parameters with observational data

This article introduces three novel parametrizations of the deceleration parameter (DP) to explore the cosmological scenario. The newly introduced parametric forms of the DP are physically plausible and model-independent. We constrained the model parameters using a Markov Chain Monte Carlo (MCMC) method by utilizing a combined dataset of 31 cosmic chronometers (CC) data points, 26 non-correlated baryonic acoustic oscillations (BAO) points, and recently updated 1701 Pantheon+ data points from supernovae type Ia (SNeIa). To explore the kinematic behavior of the model, we analyze various aspects such as the transition from deceleration to acceleration, as well as the energy conditions. The current analysis of the three parametric models reveals that the Universe is currently in an accelerated phase, which is supported by the present values of the EoS parameter and the negative SEC behavior within specific redshift ranges for all three models. Additionally, all three parametric models meet the WEC, DEC, and NEC conditions across the entire range of redshift values. We use the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) model selection criteria to compare the performance of the models. Overall, this study offers valuable insights into the accelerating Universe and emphasizes the significance of adopting a model-independent approach in cosmological investigations.

Sex-specific reference values for total, central, and peripheral latency of motor evoked potentials from a large cohort.

Differentiating between physiologic and altered motor evoked potentials (MEPs) to transcranial magnetic stimulation (TMS) is crucial in clinical practice. Some physical characteristics, such as height and age, introduce sources of variability unrelated to neural dysfunction. We provided new age- and height-adjusted normal values for cortical latency, central motor conduction time (CMCT), and peripheral motor conduction time (PMCT) from a large cohort of healthy subjects. Previously reported data from 587 participants were re-analyzed. Nervous system disorders were ruled out by clinical examination and magnetic resonance imaging. MEP latency was determined as stimulus-to-response latency through stimulation with a circular coil over the "hot spot" of the First Dorsal Interosseous and Tibialis Anterior muscles, during mild tonic contraction. CMCT was estimated as the difference between MEP cortical latency and PMCT by radicular magnetic stimulation. Additionally, right-to-left differences were calculated. For each parameter, multiple linear regression models of increasing complexity were fitted using height, age, and sex as regressors. Motor evoked potential cortical latency, PMCT, and CMCT were shown to be age- and height-dependent, although age had only a small effect on CMCT. Relying on Bayesian information criterion for model selection, MEP cortical latency and PMCT were explained best by linear models indicating a positive correlation with both height and age. Also, CMCT to lower limbs positively correlated with height and age. CMCT to upper limbs positively correlated to height, but slightly inversely correlated to age, as supported by non-parametric bootstrap analysis. Males had longer cortical latencies and CMCT to lower limbs, as well as longer PMCT and cortical latencies to upper limbs, even when accounting for differences in body height. Right-to-left-differences were independent of height, age, and sex. Based on the selected regression models, sex-specific reference values were obtained for all TMS-related latencies and inter-side differences, with adjustments for height and age, where warranted. A significant relationship was observed between height and age and all MEP latency values, in both upper and lower limbs. These set of reference values facilitate the evaluation of MEPs in clinical studies and research settings. Unlike previous reports, we also highlighted the contribution of sex.

Biotic and Abiotic Factors Affecting Short‐Term Survival of Two Age‐0 Rainbow Trout Strains in Colorado Streams

AbstractBoth biotic and abiotic factors can influence the survival and growth of age‐0 salmonids. Diseases, such as whirling disease, can also affect salmonid demographics and population dynamics. Here, we conducted a supplementary analysis and evaluated specific stream characteristics that may have been responsible for the differences in growth and survival of two whirling disease resistant Rainbow Trout Oncorhynchus mykiss strains observed by Avila et al. (2018). We used regression modeling to analyze the influence of the biotic and abiotic characteristics of nine streams on the short‐term apparent survival and growth of two Rainbow Trout strains, 5,000 German Rainbow Trout and 5,000 German Rainbow Trout × Colorado River Rainbow Trout in each stream. Akaike's information criterion (AICc) model selection was used to identify the factors that most affected short‐term survival and growth. Average stream temperature had the largest (positive) effect, βtemp = 0.060, on short‐term survival. Rainbow Trout strain, average stream temperature (βtemp = 1.55), competitor biomass (βcompetitor biomass = −0.002), and predator number (βpredator number = 0.01) additively affected short‐term growth. Our results indicate that both biotic and abiotic factors are important short‐term determinants of Rainbow Trout poststocking performance and may account for the differences in survival and growth that we observed among stocking locations.

Data science for thermodynamic modeling: Case study for ionic liquid and hydrofluorocarbon refrigerant mixtures

The sustainable phaseout of high global warming potential hydrofluorocarbon (HFC) refrigerant mixtures requires novel solvents, such as ionic liquids (ILs), for new HFC reuse and recycle technologies. Accurate, predictive, and interpretable thermodynamic models for HFC/IL mixtures are essential for multiscale design schemes aiding this phaseout. Still, there is limited guidance regarding the best thermodynamic model for an HFC/IL system. We propose a rigorous thermodynamic model selection and analysis workflow for HFC/IL mixtures which harnesses data science tools – visualization, nonlinear regression, Akaike information criteria, Fischer information matrix (FIM)-based identifiability and uncertainty analyses, and model-based design of experiments methods – to evaluate the accuracy, predictive capability, and interpretability of a thermodynamic model. The open-source IDAES™ platform facilitates training and comparison of sixteen candidate HFC/IL thermodynamic models, including two cubic equations of state, Peng–Robinson and Soave–Redlich–Kwong, and eight variations on temperature dependence within a classical van der Waals mixing rule. We apply this analysis to models for three HFC/IL systems: HFC-32/[emim][TF2N], HFC-125/[emim][TF2N], and HFC-32/[bmim][PF6]. For these mixtures, we observe that models with a temperature dependent mixing rule are consistently ranked higher by Akaike information criteria for model selection. However, these models may still have high parameter uncertainty and correlation, indicating that data at multiple temperatures should be obtained. This result differs from the current practice of generating single isotherm dataset for most new HFC/IL mixtures. Additionally, we find that the most valuable experiments are taken at the bounds of composition, temperature (e.g., 273 and 348 K), and pressure (e.g., 1 MPa) measurements. This analysis guides data generation efforts, showing that optimally selected measurements across multiple temperatures are adequate for regressing thermodynamic models for multiscale process design.

Can T1-Weighted Magnetic Resonance Imaging Significantly Improve Mini-Mental State Examination-Based Distinguishing Between Mild Cognitive Impairment and Early-Stage Alzheimer's Disease?

Detecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process. Our assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine. The multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model. The best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis. The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening.

Macromineral requirements for maintenance and growth in male and female hair sheep

A better understanding of the nutritional requirements of sheep, especially in terms of minerals, is crucial for improving production. We estimated the net requirements for Ca, P, K, Mg, and Na for gain (NCag, NPg, NKg, NMgg, and NNag) and maintenance (NCam, NPm, NKm, NMgm, and NNam) in male and female hair sheep. Six datasets with 248 individual records of hair sheep (139 non-castrated males, 75 castrated males and 34 females) were used to estimate the net macromineral requirements for gain. To estimate the net macromineral requirements for maintenance, 52 observations (26 non-castrated and 26 castrated males) were used. A meta-analytical approach was applied, using non-linear mixed effects models and the study as a random effect. Based on information criteria for model selection, heterogeneous variance functions were more likely to describe mineral requirements with a low level of model selection uncertainty. The adopted criteria allowed the choice of the best models to represent the macromineral requirements. The chosen models explained the observed variability in the sex, and the choices were based on a low level of uncertainty (w ≥ 0.90). Irrespective of sex, NCag and NPg decreased with increasing BW from 10 to 30 kg and average daily gain (ADG) of 150 g/day, ranging from 1.71-1.38; 1.83-1.57; 1.82-1.51 of Ca and 0.86-0.66; 0.92-0.78; 0.92-0.75 of P for non-castrated males, castrated males, and females, respectively. The NKg remained constant, with mean values of 0.26 g/day. The NNag range was 0.17 to 0.14 g/day for non-castrated males, 0.20 to 0.25 g/day for females, and constant (0.18 g/day) for castrated males with an increase in BW from 10 to 30 kg and an ADG of 150 g/day. Macromineral requirements for maintenance (mg/kg BW) and retention (%) were 23.70 and 54.30 for Ca, 25.33 and 79.80 for P, 11.74 and 5.00 for K, 2.63 and 8.50 for Mg, and 7.01 and 8.10 for Na for males. The International Committees did not provide inferences about the sex influence on mineral requirements. Our study indicates that sex is one factor that influences the macromineral requirements for gain. The information generated in this study can be used to optimize the mineral management of hair sheep in the growing phase in tropical regions.

Information criteria for model selection

AbstractThe rapid development of modeling techniques has brought many opportunities for data‐driven discovery and prediction. However, this also leads to the challenge of selecting the most appropriate model for any particular data task. Information criteria, such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), have been developed as a general class of model selection methods with profound connections with foundational thoughts in statistics and information theory. Many perspectives and theoretical justifications have been developed to understand when and how to use information criteria, which often depend on particular data circumstances. This review article will revisit information criteria by summarizing their key concepts, evaluation metrics, fundamental properties, interconnections, recent advancements, and common misconceptions to enrich the understanding of model selection in general.This article is categorized under: Data: Types and Structure > Traditional Statistical Data Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical and Graphical Methods of Data Analysis > Information Theoretic Methods Statistical Models > Model Selection

Ant Diversity Is Enhanced by Ecological Infrastructures in Agroecosystems: A Case Study in Irrigated Mediterranean Farmland

We aimed at assessing the role of ecological infrastructures (EI) in promoting ant biodiversity in floodplain Mediterranean agricultural crops. We examined and compared ant communities at the interface between EI (remnant vegetation patches) and adjoining agricultural matrix (maize, rice, others) in irrigated farmland. The study was conducted in 2019, in two agricultural landscapes in the valleys of the rivers Tagus and Sorraia, Central Portugal. We used the Akaike information criterion for model selection and to distinguish among a set of possible models describing the relationship between: the ant richness in the agricultural matrix and drivers associated with the surrounding landscape and crop type; the ant richness in EI and the habitat quality of EI patches, the characteristics of the surrounding landscape, and the presence of invasive ant species. We found that: EI patches supported a higher ant diversity and an overall specialized ant community, distinctive from the agricultural matrix; location but not vegetation physiognomy influenced ant diversity; ant richness within the agricultural matrix decreased with the distance to the EI, and that this relationship was influenced by the crop type; and that ant richness in the EI was associated with the absence of the invasive Argentine ant and the area of terrestrial EI in the surrounding landscape.

Method of Quartile for determination of Weibull parameters and assessment of wind potential

Weibull Distribution is the most widely used distribution in wind power assessment. Two parameters Weibull distribution is commonly used for wind distribution modeling. The wind turbine converts wind energy into electrical energy. According to Betz law, No wind turbine can convert more than 59% of the available wind energy into electrical energy. The available method to find the parameters, e.g., Empirical Method (EM), Method of Moment (MoM), Energy Pattern Factor Method (EPFM), Maximum Likelihood Method (MLM), Modified Maximum Likelihood Method (MMLM), measure an overestimated value of wind power. An attempt has been made to develop a new method to evaluate Weibull parameters to measure wind potential close to the actual one. The new methods depend on the Quartiles of the wind distribution, Method of Quartile. Wind speed data for twelve months, January to December of 2016, for the cities Hyderabad, Karachi, and Quetta is used in this study. The new method results are compared with the five methods of Weibull parameter determination, EM, MoM, EPFM, MLM, and MMLM. The new method’s average wind speed is closer to the actual average wind speed than those measured by other methods. The Root Mean Square Error (RMSE), Mean Absolute Error (MABE), and chi-square statistic calculated by all methods are close. The Akaike Information Criterion (AIC) model selection criterion was used for each method and month. It is found that the AIC values for every month and every city are the lowest for MoQ. It also suggests that the new method, MoQ, is the best among the existing method. Keywords: Weibull Distribution, Method of Quartile, Method of Moments, Maximum Likelihood, Empirical Method, Energy Pattern Factor Method

FETSmcs: Feature-based ETS model component selection

The well-developed ETS (ExponenTial Smoothing, or Error, Trend, Seasonality) method incorporates a family of exponential smoothing models in state space representation and is widely used for automatic forecasting. The existing ETS method uses information criteria for model selection by choosing an optimal model with the smallest information criterion among all models fitted to a given time series. The ETS method under such a model selection scheme suffers from computational complexity when applied to large-scale time series data. To tackle this issue, we propose an efficient approach to ETS model selection by training classifiers on simulated data to predict appropriate model component forms for a given time series. We provide a simulation study to show the model selection ability of the proposed approach on simulated data. We evaluate our approach on the widely used M4 forecasting competition dataset in terms of both point forecasts and prediction intervals. To demonstrate the practical value of our method, we showcase the performance improvements from our approach on a monthly hospital dataset.

Widespread occurrence of anomalous C-band backscatter signals in arid environments caused by subsurface scattering

Backscatter measured by scatterometers and Synthetic Aperture Radars is sensitive to the dielectric properties of the soil and normally increases with increasing soil moisture content. However, when the soil is dry, the radar waves penetrate deeper into the soil, potentially sensing subsurface scatterers such as near-surface rocks and stones. In this paper we propose an exponential model to describe the impact of such subsurface scatterers on C-Band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on board of the METOP satellites. The model predicts an increase of the subsurface scattering contributions with decreasing soil wetness that may counteract the signal from the soil surface. This may cause anomalous backscatter signals that deteriorate soil moisture retrievals from ASCAT. We test whether this new model is able to explain ASCAT observations better than a bare soil backscatter model without a subsurface scattering term, using k-fold cross validation and the Bayesian Information Criterion for model selection. We find that arid landscapes with Leptosols and Arenosols represent ideal environmental conditions for the occurrence of subsurface scattering. Nonetheless, subsurface scattering may also become important in more humid environments during dry spells. We conclude that subsurface scattering is a widespread phenomenon that (i) needs to be accounted for in active microwave soil moisture retrievals and (ii) has a potential for soil mapping, particularly in arid and semi-arid environments.