Candidate Models Research Articles

Model averaging for right censored data with measurement error.

This paper studies a novel model averaging estimation issue for linear regression models when the responses are right censored and the covariates are measured with error. A novel weighted Mallows-type criterion is proposed for the considered issue by introducing multiple candidate models. The weight vector for model averaging is selected by minimizing the proposed criterion. Under some regularity conditions, the asymptotic optimality of the selected weight vector is established in terms of its ability to achieve the lowest squared loss asymptotically. Simulation results show that the proposed method is superior to the other existing related methods. A real data example is provided to supplement the actual performance.

Probabilistic characterisation of rock mass deformation modulus from P-wave velocity based on information criteria

ABSTRACT Determination of rock mass deformation modulus (Erm ) is an important but very challenging task in rock mechanics and rock engineering. Direct measurements of Erm by in-situ tests are expensive, time-consuming, and sometimes even impossible. Since the nondestructive and easy geophysical method is commonly used to measure the P-wave velocity (Vp ) in the field, the study has developed an empirical correlation between Erm and Vp based on 834 collected datasets by considering both inherent and transformation uncertainties. The information criteria are employed to select the best model for deriving a general empirical correlation. The maximum likelihood estimation (MLE) method and computational intelligence techniques like particle swarm optimisation (PSO), artificial bee colony (ABC), and differential evolution (DE) algorithms are used to specify the incorporated parameters of each candidate model. Based on the derived general empirical equation, a Bayesian equivalent sample approach using Markov Chain Monte Carlo (MCMC) simulation is applied to conduct probabilistic characterisation of Erm by combining prior knowledge and project-specific observed Vp data. Comparative studies of the results from theory and in-situ measured Erm values suggest that the proposed method is effective and validate the applicability of the derived general empirical correlation.

The Mathematics behind Cryptocurrencies "A Statistical Analysis of Cryptocurrencies"

This article provides a statistical approach to describe the fit of the most popular cryptocurrencies, building off a previous report, "A Statistical Analysis of Cryptocurrencies." We examined Bitcoin, Ethereum, Tether, Binance, Ripple, Cardano, Solana, and Doge coins. To model our cryptocurrencies, we utilized trading prices between 2017 and 2022 in light of historic events, such as the COVID-19 pandemic. Additionally, we performed a correlation analysis to help understand the relationship between the popular cryptos. Here, we report that the candidate distributions we fit to model the currencies needed to be more independent to describe the return of all popular cryptos. This could be due to the need for Correlation between some of these popular cryptos. We found the generalized hyperbolic and the generalized t showed the best performance of the models tested, though these approaches remained limited in their overall fitness. Their performance also varied by cryptocurrency under investigation, with Tether demonstrating the worst fit across all candidate models. Using our fit models, we also predicted the average daily returns for January 1st, 2023, to February 1st, 2023, and generally found good predictive validity. These results are critical in understanding the movements of cryptos and help better understand the risk associated with trading these currencies.

Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease.

Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLMs) like Bidirectional Encoder Representations from Transformers (BERT) have shown progress in a range of natural language processing tasks but have not yet been evaluated on adverse event (AE) detection. We adapted a new clinical LLM, University of California - San Francisco (UCSF)-BERT, to identify serious AEs (SAEs) occurring after treatment with a non-steroid immunosuppressant for inflammatory bowel disease (IBD). We compared this model to other language models that have previously been applied to AE detection. We annotated 928 outpatient IBD notes corresponding to 928 individual patients with IBD for all SAE-associated hospitalizations occurring after treatment with a non-steroid immunosuppressant. These notes contained 703 SAEs in total, the most common of which was failure of intended efficacy. Out of eight candidate models, UCSF-BERT achieved the highest numerical performance on identifying drug-SAE pairs from this corpus (accuracy 88-92%, macro F1 61-68%), with 5-10% greater accuracy than previously published models. UCSF-BERT was significantly superior at identifying hospitalization events emergent to medication use (P < 0.01). LLMs like UCSF-BERT achieve numerically superior accuracy on the challenging task of SAE detection from clinical notes compared with prior methods. Future work is needed to adapt this methodology to improve model performance and evaluation using multicenter data and newer architectures like Generative pre-trained transformer (GPT). Our findings support the potential value of using large language models to enhance pharmacovigilance.

Application of Convolutional Neural Network in Quantifying Reservoir Channel Characteristics

After many years of exploitation in the petroleum field, most of the oil fields are in advanced stages of development, with a strong non-homogeneity of the reservoir, more residual oil, and low recovery efficiency. Therefore, research on various methods has been carried out by scholars to improve the rate of recovery and to understand the distribution pattern of residual oil in reservoirs. Among the whole clastic reservoirs, fluvial reservoirs occupy a large proportion, so fluvial reservoirs will be the priority for future reservoir research in China. The key to the fine characterization of fluvial-phase reservoirs is to able to reproduce the continuous curvature of the channel, and one important parameter is the width of the channel. The width of the channel sand body is one of the key factors in designing well programs, and accurately identifying the channel boundary is the key to identifying a single channel. Traditional research methods cannot accurately characterize the continuous bending and oscillating morphology of underwater diversion channels, and it is not easy to quantitatively characterize the spatial structure. Therefore, in this paper, a deep learning method is applied to quantitatively identify the width of a single channel within an underwater diversion channel at the delta front edge. Based on the sedimentary background of the block and modern depositional studies, we established candidate models for underwater diversion channels with channel widths of 100, 130, 160, 190, 220, and 250 m based on target simulation and human–computer interactions. The results show that when the width of the underwater diversion channel is 160 m, it has the highest matching rate with the conditional data and corresponds to the actual situation. Therefore, it can be determined that it is the common width of underwater diversion channel in the study area. And it is shown that the method can accurately identify the width of underwater diversion channels, and the results provide a basis for reservoir fine characterization studies.

Colony size of amazonian stingless bees and its assessment through intrinsic parameters

Stingless bees live in populous colonies that vary in size from a few hundred individuals to tens of thousands, although information on actual colony size is limited. The objectives of this study were to determine the colony size of five Amazonian stingless bee species (Melipona flavolineata, Melipona fasciculata, Scaptotrigona aff. postica, Frieseomelitta longipes, and Plebeia minima), and to identify biological parameters that covary with colony size. The number of brood cells, adult bees, and food stocks were counted under laboratory conditions, alongside field assessments of egg-laying rate and external activity of adult workers. To identify covariates of colony size, the number of adult bees was regressed against the number of brood cells, egg-laying rate, external activity of adult workers, and food stocks, and the best candidate models were ranked using the Akaike Information Criterion. Mean (±s.d.) adult populations were: in M. flavolineata, 1046 ± 185; in M. fasciculata, 593 ± 300; in S. aff. postica, 7404 ± 1391; in F. longipes, 2425 ± 1000 and in P. minima, 405 ± 254. Thus, we showed that the external activity is the biological parameter, after the number of brood cells, that presents the best relationship with the number of adult bees, which can be easily evaluated in the field. Determining colony sizes using easily measurable biological parameters are important steps to understanding stingless bee’s life histories, ecology, and evolution, and to ease their keeping and utilization for pollination.

A novel grey fractional model based on model averaging for forecasting time series

The introduction of fractional order accumulation has played a crucial role in the development of grey forecasting methods. However, accurately identifying a single fractional order accumulation for modeling diverse sequences is challenging due to the dependence of different fractional order accumulations on data structure over time. To address this issue, we propose a novel fractional grey model abbreviated as FGMMA, incorporating a model averaging method. The new model combines existing fractional grey models by using four judgment criteria, including Akaike information criteria, Bayesian information criteria, Mallows criteria, and Jackknife criteria. Meanwhile, the cutting-edge algorithm named breed particle swarm optimization is employed to search the optimal fractional order for each candidate model to enhance the effectiveness of the designed model. Subsequently, we conduct a Monte Carlo simulation for verification and validation purposes. Finally, empirical analysis based on energy consumption in three countries is conducted to verify the applicability of the proposed model. Compared with other benchmark models, we can conclude that the proposed model outperforms the other competitive models.

Bayesian statistical model for cladding high-temperature burst under loss-of-coolant accident conditions

ABSTRACT This study developed a probabilistic determination model with respect to cladding high-temperature burst conditions based on the Bayesian statistical method to reasonably evaluate fuel behaviors under loss-of-coolant accident conditions, including fuel fragmentation, relocation, and dispersal. The candidate models (correlations) were based on the widely accepted empirical model established based on nonirradiated fuel cladding data. Explanatory variables were added to improve the applicability of these models with respect to irradiated materials and generalization performance. The posterior predictive distribution of each candidate model was evaluated using Bayesian estimation comprising 238 sets of high-temperature burst test data. The generalization performance was evaluated using information criteria. The results of model evaluation showed improved predictive performance by considering the effect of hydrogen content. A comparison with burnup as an alternative explanatory variable confirmed that hydrogen content was the better parameter and other burnup-associated effects, such as irradiation hardening of the metal matrix and oxide growth (reduction of the metal matrix), were less dominant under burst conditions.

Modeling Doxorubicin Pharmacokinetics in Multiple Myeloma Suggests Mechanism of Drug Resistance.

Multiple myeloma (MM) is a plasma cell malignancy often treated with chemotherapy drugs. Among these, doxorubicin (DOXO) is commonly employed, sometimes in combined-drug therapies, but it has to be optimally administered in order to maximize its efficacy and reduce possible side effects. To support DOXO studies and treatment optimization, here we propose an experimental/modeling approach to establish a model describing DOXO pharmacokinetics (PK) in MM cells. A series of in vitro experiments were performed in MM1R and MOLP-2 cells. DOXO was administered at two dosages (200 nM, 450 nM) at [Formula: see text] = 0 and removed at [Formula: see text] = 3 hrs. Intracellular DOXO concentration was measured via fluorescence microscopy during both drug uptake ([Formula: see text] = 0-3 hrs) and release phases ([Formula: see text] = 3-8 hrs). Four PK candidate models were identified, and were compared and selected based on their ability to describe DOXO data and numerical parameter identification. The most parsimonious model consists of three compartments describing DOXO distribution between the extracellular space, the cell cytoplasm and the nucleus, and defines the intracellular DOXO efflux rate through a Hill function, simulating a threshold/saturation drug resistance mechanism. This model predicted DOXO data well in all the experiments and provided precise parameter estimates (mean ± standard deviation coefficient of variation: 15.8 ± 12.2%). A reliable PK model describing DOXO uptake and release in MM cells has been successfully developed. The proposed PK model, once integrated with DOXO pharmacodynamics, has the potential of allowing the study and the optimization of DOXO treatment strategies in MM.

Return predictability via an long short‐term memory‐based cross‐section factor model: Evidence from Chinese stock market

AbstractThis paper proposes a cross‐section long short‐term memory (CS‐LSTM) factor model to explore the possibility of estimating expected returns in the Chinese stock market. In contrast to previous machine‐learning‐based asset pricing models that make predictions directly on equity returns, CS‐LSTM estimates are based on predictions of slope terms from Fama–MacBeth cross‐section regressions using 16 stock characteristics as factor loadings. In line with previous studies in the context of the Chinese market, we find illiquidity and short‐term momentum to be the most important factors in describing asset returns. By using 274 value‐weighted portfolios as test assets, we systematically compare the performances of CS‐LSTM and three other candidate models. Our CS‐LSTM model consistently delivers better performance than the candidate models and beats the market at all different levels of transaction costs. In addition, we observe that assets with smaller cap are favored by the model. By repeating the empirical analysis based on the top 70% of stocks, our CS‐LSTM model remains robust and consistently provides significant market‐beating performance. Our findings from the CS‐LSTM model have practical implications for the future development of the Chinese stock market and other emerging markets.

Improving the second-tier classification of methylmalonic acidemia patients using a machinelearning ensemble method.

Methylmalonic acidemia (MMA) is a disorder of autosomal recessive inheritance, with an estimated prevalence of 1:50,000. First-tier clinical diagnostic tests often return many false positives [five false positive (FP): one true positive (TP)]. In this work, our goal was to refine a classification model that can minimize the number of false positives, currently an unmet need in the upstream diagnostics of MMA. We developed machinelearning multivariable screening models for MMA with utility as a secondary-tier tool for false positives reduction. We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients, followed by additional ratio feature construction. Feature selection strategies (selection by filter, recursive feature elimination, and learned vector quantization) were used to determine the input set for evaluating the performance of 14 classification models to identify a candidate model set for an ensemble model development. Our work identified computational models that explore metabolic analytes to reduce the number of false positives without compromising sensitivity. The best results [area under the receiver operating characteristic curve (AUROC) of 97%, sensitivity of 92%, and specificity of 95%] were obtained utilizing an ensemble of the algorithms random forest, C5.0, sparse linear discriminant analysis, and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the supervisor. The model achieved a good performance trade-off for a screening application with 6% false-positive rate (FPR) at 95% sensitivity, 35% FPR at 99% sensitivity, and 39% FPR at 100% sensitivity. The classification results and approach of this research can be utilized by clinicians globally, to improve the overall discovery of MMA in pediatric patients. The improved method, when adjusted to 100% precision, can be used to further inform the diagnostic process journey of MMA and help reduce the burden for patients and their families.

Detecting and counting sorghum aphid alates using smart computer vision models

Sorghum aphid [Melanaphis sorghi (Theobald)] is considered an economic pest causing significant yield losses in susceptible sorghum in the southern U.S. Infestations start with the migration of alates (i.e., winged adults) to sorghum and establishing aphid colonies. In favorable conditions, sorghum aphid can exponentially reproduce via asexual reproduction. A suggested strategy is to monitor alates to determine initial infestations and take preventive strategies, which can result in more efficient pest monitoring and management. To reduce the time of monitoring and better understand of alate establishment under field conditions, we propose using computer vision models, specifically deep learning, to detect and count alates using field-collected images. During pest monitoring, we captured 2527 images and assessed the performance of five models within the YOLOv5 architecture family using two different image sizes, including input resolutions of 640 × 640, and 1280 × 1280 pixels. We trained models to detect and count individual alates, which ranged between 1 and 100 alates/leaf. Among models, the YOLOv5l Pytorch detection model had the best overall performance at 1280 × 1280 input pixel resolution. The YOLOv5l model is a candidate model for quantifying alates on sorghum leaves using deep learning with a precision of 83.80%, 85.60% recall, and 89% mAP@0.5 with a lower mean percent error of misdetection. To enable the use of our best deep learning model by the research community, we developed a web-based application that is freely available to the public. Using this application, users can upload images to detect and count alates with a low error of misdetection.

Building a reference indicator model using co-kriging interpolation to determine the geographical origin of the flighted spongy moth complex in China.

Using stable isotopes to detect and analyze the geographical origin of insects represents an important traceability technology, which requires a rich isotope database. In this study, we representatively sampled the Chinese provinces where flighted spongy moth complex (FSMC) has been reported and, for the first time, used co-kriging interpolation to predict the distribution patterns of FSMC δ13C values in the main distribution areas. From 2020 to 2022, we set up 60 traps in 12 provinces and cities in China and collected 795 FSMCs. Then, 6 main climatic factors were obtained by multi-collinearity screening from 21 types of meteorological data collected at the sample plots, and a correlation analysis was carried out by combining longitude, latitude, and altitude data with the δ13C values of FSMC. Next, we performed a co-kriging interpolation using the 2 climatic factors with the highest correlation (isothermality and altitude) and the δ13C values of FSMC. A cross-validation was performed to systematically test 11 candidate models and select the best semi-variogram model ("Exponential"), which was then used to build a co-kriging interpolation model. The geographical distribution patterns of the FSMC δ13C values obtained from the 2 interpolation models (i.e., interpolated with isothermality and altitude, respectively) were almost the same. Moreover, the δ13C values varied significantly at the regional scale, showing regular changes in spatial distribution. Overall, the reference indicator map of the δ13C values generated from stable isotopes can be used to greatly improve the efficiency of discrimination analyses on the geographical origin of FSMC.

Triage in major incidents: development and external validation of novel machine learning-derived primary and secondary triage tools

BackgroundMajor incidents (MIs) are an important cause of death and disability. Triage tools are crucial to identifying priority 1 (P1) patients—those needing time-critical, life-saving interventions. Existing expert opinion-derived tools have...

Improving performance of bucket-type hydrological models in high latitudes with multi-model combination methods: Can we wring water from a stone?

Multi-model combination (averaging) methods (MMCMs) are used to improve the accuracy of hydrological (precipitation-runoff) outputs in simulation or forecasting/prediction modes. In this paper, we examined if the application of MMCMs can improve model performance in reproducing distributions of hydrological signatures, such as annual maxima or minima of varying durations. To this end, ten MMCMs were applied to 29 bucket-type models to simulate runoff in 50 high-latitude catchments. The MMCMs were evaluated by comparing the resulting simulated flows to the reference (i.e., best-performing) individual model, considering various commonly used performance indicators, as well as model performance in reproducing the distributions of signatures. Additionally, we analysed whether (1) the selection of the candidate models, or (2) targeting specific signatures, such as annual maxima or minima, can improve performance of the model combinations. The results suggest that the application of MMCMs can improve accuracy of runoff simulations in terms of traditional performance indicators, but fails to improve performance in reproducing the distributions of signatures. Neither excluding poor-performing models nor applying the MMCMs with the targeted signatures, improves this aspect of model performance. These findings clearly reveal the need for further research aiming at enhancing model performance in reproducing the distributions of hydrological signatures, which is essential for climate-change impact studies.

Applying models of care for total hip and knee arthroplasty: External validation of a published predictive model to identify extended stay risk prior to lower-limb arthroplasty.

This study aimed to externally validate a reported model for identifying patients requiring extended stay following lower limb arthroplasty in a new setting. External validation of a previously reported prognostic model, using retrospective data. Medium-sized hospital orthopaedic department, Australia. Electronic medical records were accessed for data collection between Sep-2019 and Feb-2020 and retrospective data extracted from 200 randomly selected total hip or knee arthroplasty patients. Participants received total hip or knee replacement between 2-Feb-16 and 4-Apr-19. This study was a non-interventional retrospective study. Model validation was assessed with discrimination, calibration on both original and adjusted forms of the candidate model. Decision curve analysis was conducted on the outputs of the adjusted model to determine net benefit at a predetermined decision threshold (0.5). The original model performed poorly, grossly overestimating length of stay with mean calibration of -3.6 (95% confidence interval -3.9 to -3.2) and calibration slope of 0.52. Performance improved following adjustment of the model intercept and model coefficients (mean calibration 0.48, 95% confidence interval 0.16 to 0.80 and slope of 1.0), but remained poorly calibrated at low and medium risk threshold and net benefit was modest (three additional patients per hundred identified as at-risk) at the a-priori risk threshold. External validation demonstrated poor performance when applied to a new patient population and would provide limited benefit for our institution. Implementation of predictive models for arthroplasty should include practical assessment of discrimination, calibration and net benefit at a clinically acceptable threshold.

Predicting long-term time to cardiovascular incidents using myocardial perfusion imaging and deep convolutional neural networks

Myocardial perfusion imaging (MPI) is a clinical tool which can assess the heart's perfusion status, thereby revealing impairments in patients' cardiac function. Within the MPI modality, the acquired three-dimensional signals are typically represented as a sequence of two-dimensional grayscale tomographic images. Here, we proposed an end-to-end survival training approach for processing gray-scale MPI tomograms to generate a risk score which reflects subsequent time to cardiovascular incidents, including cardiovascular death, non-fatal myocardial infarction, and non-fatal ischemic stroke (collectively known as Major Adverse Cardiovascular Events; MACE) as well as Congestive Heart Failure (CHF). We recruited a total of 1928 patients who had undergone MPI followed by coronary interventions. Among them, 80% (n = 1540) were randomly reserved for the training and 5- fold cross-validation stage, while 20% (n = 388) were set aside for the testing stage. The end-to-end survival training can converge well in generating effective AI models via the fivefold cross-validation approach with 1540 patients. When a candidate model is evaluated using independent images, the model can stratify patients into below-median-risk (n = 194) and above-median-risk (n = 194) groups, the corresponding survival curves of the two groups have significant difference (P < 0.0001). We further stratify the above-median-risk group to the quartile 3 and 4 group (n = 97 each), and the three patient strata, referred to as the high, intermediate and low risk groups respectively, manifest statistically significant difference. Notably, the 5-year cardiovascular incident rate is less than 5% in the low-risk group (accounting for 50% of all patients), while the rate is nearly 40% in the high-risk group (accounting for 25% of all patients). Evaluation of patient subgroups revealed stronger effect size in patients with three blocked arteries (Hazard ratio [HR]: 18.377, 95% CI 3.719–90.801, p < 0.001), followed by those with two blocked vessels at HR 7.484 (95% CI 1.858–30.150; p = 0.005). Regarding stent placement, patients with a single stent displayed a HR of 4.410 (95% CI 1.399–13.904; p = 0.011). Patients with two stents show a HR of 10.699 (95% CI 2.262–50.601; p = 0.003), escalating notably to a HR of 57.446 (95% CI 1.922–1717.207; p = 0.019) for patients with three or more stents, indicating a substantial relationship between the disease severity and the predictive capability of the AI for subsequent cardiovascular inciidents. The success of the MPI AI model in stratifying patients into subgroups with distinct time-to-cardiovascular incidents demonstrated the feasibility of proposed end-to-end survival training approach.

Establishing RTS,S/AS01 as a benchmark for comparison to next-generation malaria vaccines in a mouse model

New strategies are needed to reduce the incidence of malaria, and promising approaches include vaccines targeting the circumsporozoite protein (CSP). To improve upon the malaria vaccine, RTS,S/AS01, it is essential to standardize preclinical assays to measure the potency of next-generation vaccines against this benchmark. We focus on RTS,S/AS01-induced antibody responses and functional activity in conjunction with robust statistical analyses. Transgenic Plasmodium berghei sporozoites containing full-length P. falciparum CSP (tgPb-PfCSP) allow two assessments of efficacy: quantitative reduction in liver infection following intravenous challenge, and sterile protection from mosquito bite challenge. Two or three doses of RTS,S/AS01 were given intramuscularly at 3-week intervals, with challenge 2-weeks after the last vaccination. Minimal inter- and intra-assay variability indicates the reproducibility of the methods. Importantly, the range of this model is suitable for screening more potent vaccines. Levels of induced anti-CSP antibody 2A10 equivalency were also associated with activity: 105 μg/mL (95% CI: 68.8, 141) reduced liver infection by 50%, whereas 285 μg/mL (95% CI: 166, 404) is required for 50% sterile protection from mosquito bite challenge. Additionally, the liver burden model was able to differentiate between protected and non-protected human plasma samples from a controlled human malaria infection study, supporting these models’ relevance and predictive capability. Comparison in animal models of CSP-based vaccine candidates to RTS,S/AS01 is now possible under well controlled conditions. Assessment of the quality of induced antibodies, likely a determinant of durability of protection in humans, should be possible using these methods.

Mean size at sexual maturity of female Cynoscion reticulatus along the Pacific coast off Mexico

Drums and croakers (Sciaenidae) are important to the species composition of coastal catches in the Gulf of California, Mexico. Among these species, the striped corvina (Cynoscion reticulatus) stands out because of its high fishery potential, high demand, and increased market value. The striped croaker has been under constant fishing pressure over the past 20 years. Still, there needs to be current biological information or reference points to allow the establishment of management measures for fisheries. The objective of the present study was to estimate the mean standard length at sexual maturity (SL50) of females using various models and to suggest a minimum catch size (MCS) for C. reticulatus off the southern coast of Sinaloa. Monthly samplings of coastal fishery landings were done from December 2020 to December 2021 on the south coast of Sinaloa. The SL50 was evaluated based on 3 candidate models (Gompertz, Lysack, and White) through a binomial approach. Confidence intervals for SL50 were estimated using likelihood profiles and contours, and Akaike’s information criterion (AIC) was used to select the best model. A total of 284 individuals were examined, of which 131 were used in calculations. White’s model was the most adequate; however, based on AIC weights, all 3 models performed similarly, and SL50 (24.48 cm; C.I. 95% 12.9–70 36.0 cm) was estimated as their average. As a management measure, we propose a precautionary MCS of 36 cm SL for C. reticulatus off southern Sinaloa.

The Best Fit Bayesian Hierarchical Generalized Linear Model Selection Using Information Complexity Criteria in the MCMC Approach

Both frequentist and Bayesian statistics schools have improved statistical tools and model choices for the collected data or measurements. Model selection approaches have advanced due to the difficulty of comparing complicated hierarchical models in which linear predictors vary by grouping variables, and the number of model parameters is not distinct. Many regression model selection criteria are considered, including the maximum likelihood (ML) point estimation of the parameter and the logarithm of the likelihood of the dataset. This paper demonstrates the information complexity (ICOMP), Bayesian deviance information, or the widely applicable information criterion (WAIC) of the BRMS to hierarchical linear models fitted with repeated measures with a simulation and two real data examples. The Fisher information matrix for the Bayesian hierarchical model considering fixed and random parameters under maximizing a posterior estimation is derived. Using Gibbs sampling and Hybrid Hamiltonian Monte Carlo approaches, six different models were fitted for three distinct application datasets. The best-fitted candidate models were identified under each application dataset with the two MCMC approaches. In this case, the Bayesian hierarchical (mixed effect) linear model with random intercepts and random slopes estimated using the Hamiltonian Monte Carlo method best fits the two application datasets. Information complexity (ICOMP) is a better indicator of the best-fitted models than DIC and WAIC. In addition, the information complexity criterion showed that hierarchical models with gradient-based Hamiltonian Monte Carlo estimation are the best fit and have supper convergence relative to the gradient-free Gibbs sampling methods.