Maximum Likelihood Approach Research Articles

Regression analysis of doubly censored failure time data with ancillary information.

Doubly censored failure time data occur in many areas and for the situation, the failure time of interest usually represents the elapsed time between two related events such as an infection and the resulting disease onset. Although many methods have been proposed for regression analysis of such data, most of them are conditional on the occurrence time of the initial event and ignore the relationship between the two events or the ancillary information contained in the initial event. Corresponding to this, a new sieve maximum likelihood approach is proposed that makes use of the ancillary information, and in the method, the logistic model and Cox proportional hazards model are employed to model the initial event and the failure time of interest, respectively. A simulation study is conducted and suggests that the proposed method works well in practice and is more efficient than the existing methods as expected. The approach is applied to an AIDS study that motivated this investigation.

Bayesian inference for the inverse Weibull distribution based on symmetric and asymmetric balanced loss functions with application

In this study, the unified hybrid censored approach is employed to estimate the parameters of the inverse Weibull distribution, as well as the survival and hazard rate functions. Parameter estimates are obtained using both Bayesian and Maximum Likelihood approaches, with Bayesian estimates acquired through Lindley's approximation method using three distinct balanced loss functions. These encompass both symmetric and asymmetric balanced loss functions, specifically the balanced squared error (BSE) loss function, the balanced linear exponential (BLINEX) loss function, and the balanced general entropy (BGE) loss function. We conduct a simulation study to compare the effectiveness of various estimators, and a real-world data analysis is presented to illustrate practical implementation. Ultimately, our findings indicate that Bayesian parameter estimates consistently outperform their Maximum Likelihood counterparts across all methods.

Genomic characterization of carbapenemase-producing Klebsiella pneumoniae ST307 revealed multiple introductions in Buenos Aires, Argentina

Objective: to describe at genomic level nine carbapenemase-producing Klebsiella pneumoniae ST307 (Kp-ST307) clinical isolates recovered in Buenos Aires during 2017-2021, investigating their resistome, virulome and phylogeny.Methods: Antimicrobial susceptibility was determined according to CLSI. Genomic DNA was sequenced by Illumina MiSeq and analyzed using SPAdes, PROKKA and Kleborate. Phylogeny of 355 randomly selected Kp-ST307 genomes and those from nine local isolates was inferred by a maximum-likelihood approach. The tree was visualized using Microreact.Results: Besides resistance to ß-lactams and fluoroquinolones, six out of nine Kp-ST307 were also resistant to ceftazidime/avibactam (CZA). This difficult-to-treat resistance phenotype was mediated by blaSHV-28 and gyrA-83I/parC-80I mutations in addition to carbapenemase coding genes. Among CZA susceptible isolates, two of them harbored blaKPC-3 while the other harbored blaKPC-2+blaCTX-M-15. Respect to CZA resistant isolates, three harbored blaKPC-3+blaNDM-1+blaCMY-6, two carried blaKPC-2+blaNDM-5+blaCTX-M-15, and blaNDM-5+blaCTX-M-15 were detected in the remaining isolate. Besides, five colistin resistant isolates presented a nonsense mutation in mgrB.Global Kp-ST307 isolates were distributed in two deep-branching lineages while local isolates set in the main clade of the phylogenetic tree. The five isolates from a same hospital, harboring blaKPC-3 or blaKPC-3+blaNDM-1+blaCMY-6, clustered in a monophyletic subclade with Italian isolates. Also, an isolate harboring blaKPC-2+blaNDM-5+blaCTX-M-15 recovered in another hospital was closed to this group. The remaining local Kp-ST307 grouped in other subclades containing isolates of diverse geographically origin.Conclusion: The inferred resistome was consistent with the resistant phenotype. Phylogeny suggested multiple introduction events in our region and a single major introduction in one hospital followed by local spread.

Identification of the Aeromechanical Parameters of a Compressor Rotor from Blade Tip Timing Data

Abstract This article presents a maximum likelihood approach to identifying the mistuning, aerodynamic forcing functions, and aerodynamic influence coefficients of compressor rotor blades from noisy experimental measurements of blade deflection. The maximum likelihood estimation approach leads naturally to a nonlinear least squares optimization problem that can be solved using numerical optimization techniques such as quasi-Newton methods. An eigenmode decomposition approach is presented that permits the optimization objective function and gradient to be efficiently calculated so that the optimization is computationally feasible, even for experiments involving very large data sets. Simulation results show that the method has the potential to accurately identify the mistuning and aerodynamic nodal diameter forcing functions, but obtaining accurate results for influence coefficients may not be possible if there is significant disk flexibility. We demonstrate the method using both simulated and experimental blade vibration data from rotor 2 of the Purdue University three-stage axial compressor research facility collected using a light probe tip timing system as the rotor is slowly accelerated or decelerated through a Campbell diagram crossing.

Statistical inference of unified hybrid censoring scheme for generalized inverted exponential distribution with application to COVID-19 data

Using a unified hybrid censoring scheme, this study explores statistical inferences for products with lifetimes following the generalized inverted exponential distribution. The model’s parameters are estimated using the maximum likelihood approach. In addition, likelihood functions and asymptotic theories are employed to generate approximate confidence intervals. Moreover, Bayesian estimates based on classical likelihood functions are investigated, considering both asymmetric and symmetric loss functions with prior information. It is recommended to approximate the Bayes estimates using Gibbs sampling, which utilizes the Markov chain Monte Carlo technique to establish credible intervals for the parameters. A numerical example is provided to demonstrate the effectiveness of the proposed methods. Furthermore, a simulation study is conducted to illustrate the confidence intervals and the statistical characteristics of the parameters.

Semiparametric accelerated failure time models under unspecified random effect distributions

Accelerated failure time (AFT) models with random effects, a useful alternative to frailty models, have been widely used for analyzing clustered (or correlated) time-to-event data. In the AFT model, the distribution of the unobserved random effect is conventionally assumed to be parametric, often modeled as a normal distribution. Although it has been known that a misspecfied random-effect distribution has little effect on regression parameter estimates, in some cases, the impact caused by such misspecification is not negligible. Particularly when our focus extends to quantities associated with random effects, the problem could become worse. In this paper, we propose a semi-parametric maximum likelihood approach in which the random-effect distribution under the AFT models is left unspecified. We provide a feasible algorithm to estimate the random-effect distribution as well as model parameters. Through comprehensive simulation studies, our results demonstrate the effectiveness of this proposed method across a range of random-effect distribution types (discrete or continuous) and under conditions of heavy censoring. The efficacy of the approach is further illustrated through simulation studies and real-world data examples.

Estimations with Step-Stress Partially Accelerated Life Tests for Ailamujia Distribution under Type-I Censored Data

This paper addresses the problem of estimating parameters in partial accelerated life tests following the Ailamujia distribution under Type-I censoring, employing both the maximum likelihood approach and the least squares method. The assessment of estimation methods involves a comprehensive simulation study, complemented by the analysis of a real dataset for illustrative purposes. The findings reveal the least square estimation method outperforms maximum likelihood estimation, considering biases and mean square errors.

Pairwise Likelihood Estimation of the 2PL Model with Locally Dependent Item Responses

The local independence assumption is crucial for the consistent estimation of item parameters in item response theory models. This article explores a pairwise likelihood estimation approach for the two-parameter logistic (2PL) model that treats the local dependence structure as a nuisance in the optimization function. Hence, item parameters can be consistently estimated without explicit modeling assumptions of the dependence structure. Two simulation studies demonstrate that the proposed pairwise likelihood estimation approach allows nearly unbiased and consistent item parameter estimation. Our proposed method performs similarly to the marginal maximum likelihood and pairwise likelihood estimation approaches, which also estimate the parameters for the local dependence structure.

Fast parameter estimation of generalized extreme value distribution using neural networks

AbstractThe heavy‐tailed behavior of the generalized extreme‐value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires and so forth. However, estimating the distribution's parameters using conventional maximum likelihood methods can be computationally intensive, even for moderate‐sized datasets. To overcome this limitation, we propose a computationally efficient, likelihood‐free estimation method utilizing a neural network. Through an extensive simulation study, we demonstrate that the proposed neural network‐based method provides generalized extreme value distribution parameter estimates with comparable accuracy to the conventional maximum likelihood method but with a significant computational speedup. To account for estimation uncertainty, we utilize parametric bootstrapping, which is inherent in the trained network. Finally, we apply this method to 1000‐year annual maximum temperature data from the Community Climate System Model version 3 across North America for three atmospheric concentrations: 289 ppm (pre‐industrial), 700 ppm (future conditions), and 1400 ppm , and compare the results with those obtained using the maximum likelihood approach.

Molecular phylogeny reveals that Lagochilus lorestanicus (Lamiaceae, Leonureae) belongs to Acanthoprasium (Lamiaceae, Marrubieae)

ABSTRACT The taxonomic classification of the recently described Lagochilus lorestanicus within the genus Lagochilus is traditionally based on its morphological characters. We present here a phylogenetic hypothesis for the genus Lagochilus (Lamiaceae, tribe Leonureae) based on plastid (trnL-trnF and matK) and nuclear (ITS) DNA sequence data, using maximum parsimony, maximum likelihood and Bayesian approaches. Our analysis indicates that while representative species from the Iranian flora are assembled in a sub-clade nested in the clade Spinosi, L. lorestanicus is retrieved in the Acanthoprasium clade in the tribe Marrubieae. The taxonomic placement of Lagochilus lorestanicus in Acanthoprasium is also corroborated by re-examination of morphological characters. Consequently, the species is transferred to the genus Acanthoprasium, which was previously restricted to Italy and France (i.e. A. frutescens) and Cyprus (i.e. A. integrifolium). The intra-specific morphological variations as well as the conservation status of Lagochilus lorestanicus are discussed.

Trace elemental and stable isotopic signatures to reconstruct the large-scale environmental connectivity of fish populations

Animal movements and connectivity across different environments constitute essential components of virtually all ecological and evolutionary processes. In the present study, we measured multiple trace-element profiles and stable isotopes to reconstruct life cycle patterns of Eleutheronema rhadinum, and patterns of connectivity among populations of E. rhadinum across 3 distinct ecoregions in the eastern China Sea. Our results indicated that E. rhadinum larvae from different regions exhibited distinct otolith core levels of trace elements and stable isotopes, serving as discriminative markers of geographical origin. We used the maximum likelihood approach and linear discriminant analysis to trace back the origins of the mixed adult fish stocks. After leaving their nursery areas, juvenile fish from colder northern regions migrated south to overwinter in the South China Sea, where they mixed with the local populations. After overwintering, the adult fish began their long journey north, with the migrating population including individuals of both East China Sea and South China Sea origins. Meanwhile, fish in the Beibu Gulf exhibited a lower degree of interaction with the South China Sea populations and minimal interaction with those in the East China Sea. Trace elements and stable isotopes revealed the spatiotemporal connectivity of E. rhadinum across the 3 different ecoregions. This study provides critical information on how marine connectivity supports temperature-mediated fish community movement patterns within coastal ecosystems.

A maximum likelihood and regenerative bootstrap approach for estimation and forecasting of INAR(p) processes with zero-inflated innovations

In this work, we study a class of p-order non-negative integer-valued autoregressive (INAR(p)) processes, with innovations following zero-inflated (ZI) distributions called ZI-INAR(p) processes. Based on the EM algorithm, we present an estimation procedure of parameters model. We also develop a regenerative bootstrap method to construct confidence intervals for the parameters as well as to estimate the forecasting distributions for future values. We discuss asymptotic properties of the regenerative bootstrap method. The performance of the proposed methods is evaluated considering the analysis of two simulation studies and a real dataset.

Fitting various medical and engineering applications using the new lifetime distribution with a mathematical framework

Sustainability considerations play a crucial role in informing the modeling and fitting of medical and engineering data, ensuring the development of robust and environmentally conscious solutions. This paper delves into the investigation of a novel continuous distribution, aiming to provide a thorough understanding of its various fundamental mathematical and statistical properties. The analysis encompasses an exploration of survival functions, hazard rate functions, quantile, skewness, kurtosis, moments, mean time to failure, mean time to repair, insurance pricing principles, availability, and mean residual (past) lifetime functions. The proposed model demonstrates versatility in modeling both asymmetric and symmetric data across various kurtosis shapes. It can effectively handle outlier observations and accommodate different shapes of failure rates, including unimodal, bathtub, increasing, or decreasing patterns. This makes the proposed model suitable for modeling data in diverse fields. The maximum likelihood approach is employed to estimate model parameters using complete and upper recorded values. A simulation study is conducted to evaluate the performance of the estimators under different sample sizes for both complete and upper recorded values. To further demonstrate the flexibility and effectiveness of the new model, two datasets from medical and engineering domains are utilized for validation and testing purposes.

Using maximum likelihood estimation approach to adjust parameters of CARTE thermochemical code.

AbstractUsing thermochemical codes to predict the properties of detonation products and the parameters for detonation modeling of explosives requires the use of very precise theoretical equations of state (EoS). We share the updates to the thermochemical code CARTE, which allows the calculation of thermodynamic properties and chemical compositions of detonation products over a wide range of temperatures and densities. In this paper, we focus on the theoretical EoS of pure fluids, based on the KLRR perturbation method and EXP‐6 potentials. We propose a maximum likelihood approach to fit the EXP‐6 parameters by matching a combination of shock data and static compression data. We present the results obtained on the detonation properties on nitric oxide.

MAST: Phylogenetic Inference with Mixtures Across Sites and Trees.

Hundreds or thousands of loci are now routinely used in modern phylogenomic studies. Concatenation approaches to tree inference assume that there is a single topology for the entire dataset, but different loci may have different evolutionary histories due to incomplete lineage sorting (ILS), introgression, and/or horizontal gene transfer; even single loci may not be treelike due to recombination. To overcome this shortcoming, we introduce an implementation of a multi-tree mixture model that we call mixtures across sites and trees (MAST). This model extends a prior implementation by Boussau et al. (2009) by allowing users to estimate the weight of each of a set of pre-specified bifurcating trees in a single alignment. The MAST model allows each tree to have its own weight, topology, branch lengths, substitution model, nucleotide or amino acid frequencies, and model of rate heterogeneity across sites. We implemented the MAST model in a maximum-likelihood framework in the popular phylogenetic software, IQ-TREE. Simulations show that we can accurately recover the true model parameters, including branch lengths and tree weights for a given set of tree topologies, under a wide range of biologically realistic scenarios. We also show that we can use standard statistical inference approaches to reject a single-tree model when data are simulated under multiple trees (and vice versa). We applied the MAST model to multiple primate datasets and found that it can recover the signal of ILS in the Great Apes, as well as the asymmetry in minor trees caused by introgression among several macaque species. When applied to a dataset of 4 Platyrrhine species for which standard concatenated maximum likelihood (ML) and gene tree approaches disagree, we observe that MAST gives the highest weight (i.e., the largest proportion of sites) to the tree also supported by gene tree approaches. These results suggest that the MAST model is able to analyze a concatenated alignment using ML while avoiding some of the biases that come with assuming there is only a single tree. We discuss how the MAST model can be extended in the future.

An encoding approach for stable change point detection

Without imposing prior distributional knowledge underlying multivariate time series of interest, we propose a nonparametric change-point detection approach to estimate the number of change points and their locations along the temporal axis. We develop a structural subsampling procedure such that the observations are encoded into multiple sequences of Bernoulli variables. A maximum likelihood approach in conjunction with a newly developed searching algorithm is implemented to detect change points on each Bernoulli process separately. Then, aggregation statistics are proposed to collectively synthesize change-point results from all individual univariate time series into consistent and stable location estimations. We also study a weighting strategy to measure the degree of relevance for different subsampled groups. Simulation studies are conducted and shown that the proposed change-point methodology for multivariate time series has favorable performance comparing with currently available state-of-the-art nonparametric methods under various settings with different degrees of complexity. Real data analyses are finally performed on categorical, ordinal, and continuous time series taken from fields of genetics, climate, and finance.

Evolutionary relationships, hybridization and diversification under domestication of the locoto chile (Capsicum pubescens) and its wild relatives.

Patterns of genetic variation in crops are the result of multiple processes that have occurred during their domestication and improvement, and are influenced by their wild progenitors that often remain understudied. The locoto chile, Capsicum pubescens, is a crop grown mainly in mid-highlands of South-Central America. This species is not known from the wild and exists only as a cultigen. The evolutionary affinities and exact origin of C. pubescens have still not been elucidated, with hypotheses suggesting its genetic relatedness and origin to two wild putative ancestral Capsicum species from the Central Andes, C.eximium and C. cardenasii. In the current study, RAD-sequencing was applied to obtain genome-wide data for 48 individuals of C. pubescens and its wild allies representing different geographical areas. Bayesian, Maximum Likelihood and coalescent-based analytical approaches were used to reconstruct population genetic patterns and phylogenetic relationships of the studied species. The results revealed that C. pubescens forms a well-defined monotypic lineage closely related to wild C. cardenasii and C. eximium, and also to C. eshbaughii. The primary lineages associated with the diversification under domestication of C. pubescens were also identified. Although direct ancestor-descendant relationship could not be inferred within this group of taxa, hybridization events were detected between C. pubescens and both C.cardenasii and C. eximium. Therefore, although hybrid origin of C. pubescens could not be inferred, gene flow involving its wild siblings was shown to be an important factor contributing to its contemporary genetic diversity. The data allowed for the inference of the center of origin of C. pubescens in central-western Bolivia highlands and for better understanding of the dynamics of its gene pool. The results of this study are essential for germplasm conservation and breeding purposes, and provide excellent basis for further research of the locoto chile and its wild relatives.

From Average Transient Transporter Currents to Microscopic Mechanism─A Bayesian Analysis.

Electrophysiology studies of secondary active transporters have revealed quantitative mechanistic insights over many decades of research. However, the emergence of new experimental and analytical approaches calls for investigation of the capabilities and limitations of the newer methods. We examine the ability of solid-supported membrane electrophysiology (SSME) to characterize discrete-state kinetic models with >10 rate constants. We use a Bayesian framework applied to synthetic data for three tasks: to quantify and check (i) the precision of parameter estimates under different assumptions, (ii) the ability of computation to guide the selection of experimental conditions, and (iii) the ability of our approach to distinguish among mechanisms based on SSME data. When the general mechanism, i.e., event order, is known in advance, we show that a subset of kinetic parameters can be "practically identified" within ∼1 order of magnitude, based on SSME current traces that visually appear to exhibit simple exponential behavior. This remains true even when accounting for systematic measurement bias and realistic uncertainties in experimental inputs (concentrations) are incorporated into the analysis. When experimental conditions are optimized or different experiments are combined, the number of practically identifiable parameters can be increased substantially. Some parameters remain intrinsically difficult to estimate through SSME data alone, suggesting that additional experiments are required to fully characterize parameters. We also demonstrate the ability to perform model selection and determine the order of events when that is not known in advance, comparing Bayesian and maximum-likelihood approaches. Finally, our studies elucidate good practices for the increasingly popular but subtly challenging Bayesian calculations for structural and systems biology.

Comparison of item response theory ability and item parameters according to classical and Bayesian estimation methods

This research aims to compare the ability and item parameter estimations of Item Response Theory according to Maximum likelihood and Bayesian approaches in different Monte Carlo simulation conditions. For this purpose, depending on the changes in the priori distribution type, sample size, test length, and logistics model, the ability and item parameters estimated according to the maximum likelihood and Bayesian method and the differences in the RMSE of these parameters were examined. The priori distribution (normal, left-skewed, right-skewed, leptokurtic, and platykurtic), test length (10, 20, 40), sample size (100, 500, 1000), logistics model (2PL, 3PL). The simulation conditions were performed with 100 replications. Mixed model ANOVA was performed to determine RMSE differentiations. The prior distribution type, test length, and estimation method in the differentiation of ability parameter and RMSE were estimated in 2PL models; the priori distribution type and test length were significant in the differences in the ability parameter and RMSE estimated in the 3PL model. While prior distribution type, sample size, and estimation method created a significant difference in the RMSE of the item discrimination parameter estimated in the 2PL model, none of the conditions created a significant difference in the RMSE of the item difficulty parameter. The priori distribution type, sample size, and estimation method in the item discrimination RMSE were estimated in the 3PL model; the a priori distribution and estimation method created significant differentiation in the RMSE of the lower asymptote parameter. However, none of the conditions significantly changed the RMSE of item difficulty parameters.

Molecular identification, phylogenetic analysis and histopathological study of pathogenic free-living amoebae isolated from discus fish (Symphysodon aequifasciatus) in Iran: 2020–2022

Free-living amoebae (FLA) are capable of inhabiting diverse reservoirs independently, without relying on a host organism, hence their designation as “free-living”. The majority of amoebae that infect freshwater or marine fish are amphizoic, or free-living forms that may colonize fish under particular circumstances. Symphysodon aequifasciatus, commonly referred to as the discus, is widely recognized as a popular ornamental fish species. The primary objective of the present study was to determine the presence of pathogenic free-living amoebae (FLA) in samples of discus fish. Fish exhibiting clinical signs, sourced from various fish farms, were transferred to the ornamental fish clinic. The skin, gills, and intestinal mucosa of the fish were collected and subjected to culturing on plates containing a 1% non-nutrient agar medium. The detection of FLA was conducted through morphological, histopathological and molecular methods. The construction of the phylogenetic tree for Acanthamoeba genotypes was achieved using the maximum likelihood approach. The molecular sequence analysis revealed that all cultures that tested positive for FLA were T4 genotype of Acanthamoeba and Acanthamoeba sp. The examination of gill samples using histopathological methods demonstrated the presence of lamellar epithelial hyperplasia, significant fusion of secondary lamellae, and infiltration of inflammatory cells. A multitude of cysts, varying in shape from circular to elliptical, were observed within the gills. The occurrence of interlamellar vesicles and amoeboid organisms could be observed within the epithelial tissue of the gills. In the current study, presence of the Acanthamoeba T4 genotype on the skin and gills of discus fish exhibiting signs of illness in freshwater ornamental fish farms was identified. This observation suggests the potential of a transmission of amoebic infection from ornamental fish to humans, thereby highlighting the need for further investigation into this infection among ornamental fish maintained as pets, as well as individuals who interact with them and their environment.