Likelihood Procedure Research Articles

A Bayesian Lasso based sparse learning model

The Bayesian Lasso is constructed in the linear regression framework and applies the Gibbs sampling to estimate the regression parameters. This paper develops a new sparse learning model, named the Bayesian Lasso Sparse (BLS) model, that takes the hierarchical model formulation of the Bayesian Lasso. The main difference from the original Bayesian Lasso lies in the estimation procedure; the BLS uses a learning algorithm based on the type-II maximum likelihood procedure. Opposed to the Bayesian Lasso, the BLS provides sparse estimates of the regression parameters. The BLS is also derived for nonlinear supervised learning problems by introducing kernel functions. We compare the BLS model to the well known Relevance Vector Machine, the Fast Laplace, the Bayesian Lasso, and the Lasso, on both simulated and real data. The numerical results show that the BLS is sparse and precise, especially when dealing with noisy and irregular dataset.

A New Modification of Rani Distribution with More Flexibility in Application

In this article, the Rani distribution has been modified to a more flexible variant. The new model is called "the XRani distribution" and it is also a one-parameter distribution just as the parent distribution-Rani. The properties of the XRani distribution were derived and an analysis of the behaviour was conducted. The parameter was estimated using the maximum likelihood procedure. Four lifetime data sets were used to illustrate the importance of the proposed model. From the results, the proposed model competes favorably among the members of the Lindley class of distributions.

Genetic parameters for lifetime productivity in Holstein and Brown Swiss dairy cows in Honduras

Objective. To estimate the heritability (h2) of age at first parturition (AFP), longevity (L), useful life (UL), and productivity (P), as well as the genetic (rg) and phenotypic correlations between those traits in Brown Swiss and Holstein cows in Northwestern Honduras. Materials and methods. Cattle were raised under tropical conditions and intensive management. The model for AFP assessed the effect of contemporary group (herd, year and birth season combination) as fixed. The model used for L, UL and P included the contemporary group, age group at first calving and their interaction. Data were analyzed using restricted maximum likelihood procedure, adjusted to a univariate and to a bivariate animal model. Results. The h2 estimated for AFP, L, UL and P were 0.16, 0.18, 0.25 and 0.09, respectively, in Brown Swiss cows, and 0.53, 0.49, 0.7 and 0.22, respectively in Holstein. Negative but favorable rg among Brown Swiss cow traits ranged from 0.31 to 0.46 between AFP and the other traits. However, for Holsteins cows, positive rg among AFP and the other traits here studied varied from 0.49 to 0.96. In both cattle breeds, rg among L, UL and P, traits were positive. Conclusions. For all traits evaluated low to medium heritability’s for BS suggest that a slow genetic improvement could be achieved if independent selection of traits is applied, and better response could be expected for H breed. For both breeds the positive rg among L, UL and P suggest thar similar selection criteria could be used.

Revisiting Vrancea (Romania) Intermediate-Depth Seismicity: Some Statistical Characteristics and Seismic Quiescence Testing

Background: The intermediate-depth seismicity in the Vrancea region (Romania) is characterized by localized and persistent earthquake activity that culminates about two or three times in a century with the occurrence of a large event (M ≥ 6.5). Here we have revisited some important seismicity characteristics, using earthquake catalog data spanning two different time periods: 1960–1999 and 2005–2013. Methods: we have determined the b-value of the frequency-magnitude distribution of earthquakes, using a maximum likelihood procedure, and estimated the parameter β to quantify anomalous seismicity rate decreases and increases. Results: by using data from the first period, we have confirmed the existence of a decreased b-value in the deepest part of the seismogenic zone; by using data from the second period, we have statistically confirmed the seismic quiescence that preceded the occurrence of the 1977 M7.4 Vrancea earthquake. Conclusions: the decreased b-value has been interpreted either in terms of an increased lithostatic stress with depth or as an indicator of the depth range where the next major Vrancea earthquake may occur. The time variation of the seismicity parameter β may reveal anomalous seismic quiescence and increased earthquake rates that may precede the occurrence of large earthquakes.

An Evaluation of Non-Iterative Estimators in the Structural after Measurement (SAM) Approach to Structural Equation Modeling (SEM)

In Structural Equation Modeling (SEM), the measurement part and the structural part are typically estimated simultaneously via an iterative Maximum Likelihood (ML) procedure. In this study, we compare performance of the standard procedure to the Structural After Measurement (SAM) approach, where the structural part is separated from the measurement part. One appealing feature of the latter multi-step procedure is that it extends the scope of possible estimators, as now also non-iterative methods from factor-analytic literature can be used to estimate the measurement models. In our simulations, the SAM approach outperformed vanilla SEM in small to moderate samples (i.e., no convergence issues, no inadmissible solutions, smaller MSE values). Notably, this held regardless of the estimator used for the measurement part, with negligible differences between iterative and non-iterative estimators. This may call into question the added value of advanced iterative algorithms over closed-form expressions (which generally require less computational time and resources).

Estimation of Genetic Parameters for Weights at Different Ages in Anatolian Buffalo Calves

Background: The determination of the breeding potential and direction of the population in terms of traits of interest depends on the genetic variation. Therefore, it is essential to know the genetic parameters of economic traits to improve yields in animal breeding. The objective of this study was to investigate the heritability, genetic and phenotypic correlations among growth traits in Anatolian buffaloes of ìstanbul province. Methods: A total records of 7649 birth weights, 4623 sixth-month weights and 3133 yearling weights belonging to buffalo calves born between 2012 and 2021 were used. Genetic and phenotypic parameters were estimated from univariate and bivariate animal model using restricted maximum likelihood (REML) procedure in Wombat software. Village, year, season, sex and age of dam were considered as fixed effects and animal additive genetic effect was taken as random effect. Result: The heritability estimates for birth, sixth-month and yearling weights were 0.14±0.03; 0.22±0.05; 0.39±0.07 respectively. Genetic and phenotypic correlations among growth traits were generally significant and positive and ranged from 0.28±0.02 to 0.97±0.02. As a consequence, the moderate to high estimates of heritability, genetic and phenotypic correlations on growth traits showed that there were some opportunities for genetic improvement in buffaloes of ìstanbul. These genetic parameters should be considered in selection programs.

A Graph-Space Optimal Transport Approach Based on Kaniadakis κ-Gaussian Distribution for Inverse Problems Related to Wave Propagation.

Data-centric inverse problems are a process of inferring physical attributes from indirect measurements. Full-waveform inversion (FWI) is a non-linear inverse problem that attempts to obtain a quantitative physical model by comparing the wave equation solution with observed data, optimizing an objective function. However, the FWI is strenuously dependent on a robust objective function, especially for dealing with cycle-skipping issues and non-Gaussian noises in the dataset. In this work, we present an objective function based on the Kaniadakis κ-Gaussian distribution and the optimal transport (OT) theory to mitigate non-Gaussian noise effects and phase ambiguity concerns that cause cycle skipping. We construct the κ-objective function using the probabilistic maximum likelihood procedure and include it within a well-posed version of the original OT formulation, known as the Kantorovich-Rubinstein metric. We represent the data in the graph space to satisfy the probability axioms required by the Kantorovich-Rubinstein framework. We call our proposal the κ-Graph-Space Optimal Transport FWI (κ-GSOT-FWI). The results suggest that the κ-GSOT-FWI is an effective procedure to circumvent the effects of non-Gaussian noise and cycle-skipping problems. They also show that the Kaniadakis κ-statistics significantly improve the FWI objective function convergence, resulting in higher-resolution models than classical techniques, especially when κ=0.6.

Physics Beyond the Standard Model with Future X-Ray Observatories: Projected Constraints on Very-light Axion-like Particles with Athena and AXIS

Axion-like particles (ALPs) are well-motivated extensions of the Standard Model of Particle Physics and a generic prediction of some string theories. X-ray observations of bright active galactic nuclei (AGNs) hosted by rich clusters of galaxies are excellent probes of very-light ALPs, with masses . We evaluate the potential of future X-ray observatories, particularly Athena and the proposed AXIS, to constrain ALPs via observations of cluster-hosted AGNs, taking NGC 1275 in the Perseus cluster as our exemplar. Assuming perfect knowledge of the instrument calibration, we show that a modest exposure (200 ks) of NGC 1275 by Athena permits us to exclude all photon–ALP couplings g aγ > 6.3 × 10−14 GeV−1 at the 95% confidence level, as previously shown by Conlon et al., representing a factor of 10 improvement over current limits. We then proceed to assess the impact of realistic calibration uncertainties on the Athena projection by applying a standard Cash likelihood procedure, showing the projected constraints on g aγ weaken by a factor of 10 (back to the current most sensitive constraints). However, we show how the use of a deep neural network can disentangle the energy-dependent features induced by instrumental miscalibration and those induced by photon–ALP mixing, allowing us to recover most of the sensitivity to the ALP physics. In our explicit demonstration, the machine learning applied allows us to exclude g aγ > 2.0 × 10−13 GeV−1, complementing the projected constraints of next-generation ALP dark matter birefringent cavity searches for very-light ALPs. Finally, we show that a 200 ks AXIS/on-axis observation of NGC 1275 will tighten the current best constraints on very-light ALPs by a factor of 3.

Genetic evaluation of growth rate and efficiency-related traits in Dorper × local crossbred sheep population.

The present study was carried out to estimate genetic and phenotypic parameters for growth rate and efficiency-related traits in Dorper crossbred sheep population. Data on body weight collected from 2012 to 2021 at Debre Birhan Agricultural Research Center, Amhara Regional State, Ethiopia, were used to estimate phenotypic and genetic parameters for daily gain from birth to weaning (DG0-3), daily gain from weaning to 6 months (DG3-6), and daily gain from 6 months to yearling (DG6-12) and corresponding Kleiber ratios (KR0-3, KR3-6, KR6-12), efficiency of growth (GE0-3, GE3-6, GE6-12), and relative growth rate (RG0-3, RG3-6, RG6-12). Genetic parameters were estimated by restricted maximum likelihood (REML) procedure fitting six different univariate animal models and the most appropriate model for each trait was determined by log-likelihood ratio test. Multivariate analysis was carried out to estimate correlations between traits. Year and season of birth had a significant effect (p<0.001) in all studied traits. Direct heritability estimates for DG0-3, DG3-6, DG6-12, KR0-3, KR3-6, KR6-12, GE0-3, GE3-6, GE6-12, GR0-3, GR3-6, and GR6-12 were 0.45±0.15, 0.04±0.06, 0.15±0.11, 0.30±0.08, 0.13±0.11, 0.14±0.12, 0.34±0.15, 0.39±0.17, 0.31±0.14, 0.25±0.08, 0.23±0.13, and 0.23±0.13 respectively. Genetic correlation estimates between DG3-6 and other traits were positive and high in magnitude to their respective growth phase (0.95, 0.86, and 0.91 for KR3-6, GE3-6, and GR3-6 respectively). As the Dorper crossbred sheep are reaches market weight at about 6 months of age, focusing on improving traits measured during weaning to 6 months of age is more feasible. Selection based on DG3-6 is recommended to improve efficiency-related traits.

Does Binaural Stimulation Enhance Temporal Processing in Young Children?

Background and objective Temporal processing abilities help perceive signal changes over time. Efficient temporal processing is necessary for pitch perception, voice identification, and speech perception. It plays a significant role in language development. Internal redundancy of the central auditory nervous plays a role in processing sensory information. There is a need to gain more insights into the maturation of neural hardwiring that supports binaural temporal processing at a young age. The purpose of the present study was to evaluate the difference between monaural and binaural temporal processing in children aged 7-11 years. Methods Temporal processing was assessed using gap detection and temporal modulation transfer function (TMTF) tests. The tests were administered in 40 typically developing children with normal clinical auditory sensitivity. The maximum likelihood procedure (MLP), a MATLAB toolbox, was employed to deliver the stimulus. A multivariate analysis followed by post hoc analysis was performed to analyze the data. Results There was a significant difference between binaural and monoaural stimulation in children aged 7-11 years. However, there was no statistically significant difference between the right and left ears for gap detection threshold (GDT) and TMTF across all test frequencies. Conclusion Based on our findings, binaural stimulation enhances temporal processing in young children.

Bayesian Latent Class Analysis: Sample Size, Model Size, and Classification Precision

The current literature includes limited information on the classification precision of Bayes estimation for latent class analysis (BLCA). (1) Objectives: The present study compared BLCA with the robust maximum likelihood (MLR) procedure, which is the default procedure with the Mplus 8.0 software. (2) Method: Markov chain Monte Carlo simulations were used to estimate two-, three-, and four-class models measured by four binary observed indicators with samples of 1000, 750, 500, 250, 100, and 75 observations, respectively. With each sample, the number of replications was 500, and entropy and average latent class probabilities for most likely latent class membership were recorded. (3) Results: Bayes entropy values were more stable and ranged between 0.644 and 1. Bayes’ average latent class probabilities ranged between 0.528 and 1. MLR entropy values ranged between 0.552 and 0.958. and MLR average latent class probabilities ranged between 0.539 and 0.993. With the two-class model, BLCA outperformed MLR with all sample sizes. With the three-class model, BLCA had higher classification precision with the 75-sample size, whereas MLR performed slightly better with the 750- and 1000-sample sizes. With the 4-class model, BLCA underperformed MLR and had an increased number of unsuccessful computations, particularly with smaller samples.

One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves

Summary This paper considers the one-step targeted maximum likelihood estimation methodology for multi-dimensional causal parameters in general survival and competing risk settings where event times take place on the positive real line and are subject to right censoring. We focus on effects of baseline treatment decisions possibly confounded by pretreatment covariates, but remark that our work generalizes to settings with time-varying treatment regimes and time-dependent confounding. We point out two overall contributions of our work. First, our methods can be used to obtain simultaneous inference for treatment effects on multiple absolute risks in competing risk settings. Second, our methods can be used to achieve inference for the full survival curve, or a full absolute risk curve, across time. The one-step targeted maximum likelihood procedure is based on a one-dimensional universal least favourable submodel for each cause-specific hazard that we implement in recursive steps along a corresponding nonuniversal multivariate least favourable submodel. Our empirical study demonstrates the practical use of the methods.

Alternative Energy and CO2 Emission in China: Evidence from Bounds Testing and Vector Error Correction Model Approach

This empirical study investigates the dynamic interconnection between fossil fuel consumption, alternative energy consumption, economic growth and carbon emissions in China over the 1981 to 2020 time period within a multivariate framework. The long-term relationships between the sequences are determined through the application of the Autoregressive Distributed Lag (ARDL) bounds test and augmented by the Johansen maximum likelihood procedure. The causal relationships between the variables are tested with the Granger causality technique based on the Vector Error Correction Model (VECM). Empirical results reveal the existence of a statistically significant negative relationship between alternative energy consumption and carbon emissions in the long-term equilibrium. Furthermore, the VECM results demonstrate that both carbon emissions and fossil fuel consumption have unidirectional effects on economic growth. Additionally, the study highlights a short-term unidirectional causal relationship from economic growth to alternative energy consumption. These findings suggest that a reduction in fossil fuel consumption in the short run may indirectly impede the development of alternative energy. The study proposes that China should expedite the development of alternative energy and control the expansion of fossil fuel consumption to attain its carbon reduction target without hindering economic growth.

Extended Inverse Weibull Distribution with Application in Reliability Sciences

This paper presents a new four-parameter lifetime model by generalizing the Inverse Weibull (IW) distribution using Transmuted Kumaraswamy family of distributions. The proposed model explains the high probability at the tail effectively. The induction of additional parameters enhance the potentiality of the IW distribution and make it a pliant model that provides a vast range of existing distributions as special cases. Several probabilistic properties of the proposed model including moments, probability weighted moments, moment generating function, quantile function, reliability measures, and order statistics are discussed. The maximum likelihood (ML) procedure is adopted for the estimation of model parameters. The efficiency of ML estimates is to testify through simulation study. Four datasets from the field of reliability science are used to expound the competence of the proposed model.

Evaluation of maximum likelihood procedures in ranked set sampling under imperfect ranking

Ranked set sampling (RSS) is a cost-efficient alternative to simple random sampling (SRS) when ranking small sets of population units is much simpler than effectively measuring them. Maximum likelihood estimation (MLE) in RSS is usually based on the restrictive assumption of perfect ranking, assuming that the sample units lead to order statistics. However, this procedure is not flexible and often produces highly biased estimators when ranking errors are present. To remedy these constraints, in this work we suggest and evaluate alternative MLE procedures for RSS: (i) MLE based on the perfect ranking assumption; (ii) MLE based on the SRS assumptions; (iii) a weighted estimator derived from (i) and (ii); (iv) the choice of (i) or (ii) by testing the null hypothesis of perfect ranking. We carried out extensive simulations to investigate the efficiency of each strategy, which allowed us to conclude that although (i) performed the best under perfect ranking, (ii-iv) are preferable under imperfect ranking. We have also evaluated a parametric bootstrap algorithm to obtain confidence intervals in RSS based on the suggested estimators. Two applications using data from age determination of fishes and anthropometric measures of athletes illustrate and corroborate our findings.

Consumer willingness to pay for production attributes of cotton apparel

AbstractWe investigate US consumers' willingness to pay for cotton apparel production and country of origin attributes. Using a choice‐based conjoint experiment and information treatments, we examine the preferences of 727 US shoppers for the attributes: cotton fiber production systems and country of manufacture of the cotton fiber. Random utility theory is the basis for the survey's responses analysis to estimate willingness to pay (WTP) values for the attributes. Choices made by consumers are modeled using a mixed logit model in WTP space estimated using simulated maximum likelihood procedures. Results show that consumers are willing to pay more for cotton apparel from the United States than apparel from other countries, and more for apparel made from fiber produced in organic systems than in conventional systems. Only some subgroups of consumers were found to be affected by exposure to an information treatment regarding potential labor exploitation in cotton farms and textile mills. [EconLit Citations: D12].

On some aspects of a bivariate alternative zero-inflated logarithmic series distribution

In this paper, we discuss some important aspects of the bivariate alternative zero-inflated logarithmic series distribution (BAZILSD) of which the marginals are the alternative zero-inflated logarithmic series distributions of Kumar and Riyaz (2015. An alternative version of zero-inflated logarithmic series distribution and some of its applications. Journal of Statistical Computation and Simulation, 85(6), 1117–1127). We study some important properties of the distribution by deriving expressions for its probability mass function, factorial moments, conditional probability generating functions, and recursion formulae for its probabilities, raw moments and factorial moments. The parameters of the BAZILSD are estimated by the method of maximum likelihood and certain test procedures are also considered. Further certain real-life data applications are cited for illustrating the usefulness of the model. A simulation study is conducted for assessing the performance of the maximum likelihood estimators of the parameters of the BAZILSD.

Genetic and phenotypic parameter estimates for selection within Ugandan indigenous chickens

The high genetic variation within indigenous chickens (IC) which provides an opportunity to select superior stock for sustainable production and conservation is under-exploited. This study is aimed at estimating heritability and genetic and phenotypic correlation coefficients of productive and reproductive traits of Ugandan IC as a basis for selection. Data on traits were collected across two consecutive generations, weight (W) and shank length (SL) of chicks at hatching (HW) as well as at 2 (W2; SL2), 4 (W4; SL4), 6 (W6; SL6), 8 (W8; SL8), and 12 (W12; SL12) weeks of growth. Body weights at onset of lay (WFE) were also measured. In addition, egg number (EN-60), egg weight (EW), clutch number (CLN-60), and clutch size (CLS-60) over a period of 60 days were recorded. Genetic parameters were estimated using the univariate animal model analysis with restricted maximum likelihood procedure using the variability package of R, version 4.1.1. Heritability of traits ranged from 0.30 and 0.72 except SL4 (0.02), SL12 (0.14), and EN-60 (0.17). The traits EN-60 and W4 were negatively phenotypically correlated (- 0.49). Body weight at first egg was highly genetically correlated (0.99) with SL8. Egg number was significantly, negatively, and genetically correlated (- 0.96) with SL12. In conclusion, shank length is a potential phenotypic marker when selecting for live weight at onset of lay and egg yield. The shank length could, therefore, permit selection of superior chickens at an early age.

Heritability and environmental influence on pre-weaning traits in Kilis goats

Birth and weaning weights, average daily weight gain, and Kleiber ratio are important indicator traits in selection decision. The phenotypic expression of these traits is determined by the genetic background, environmental effects, and their interactions. The objective of this study was to estimate genetic parameters regarding birth (BW) and weaning weights (WW) and average daily weight gain (ADWG), Kleiber ratio (KR), and obtain the effects of sex, birth type, herd, and year. The data consisted of 2274 Kilis goats with pedigree information obtained from 53 bucks and 774 does in 4 generations. The restricted maximum likelihood (REML) procedure was conducted with an animal linear mixed model. Sex, birth type, herd, and year were found to be statistically significant (p value < 0.001) for all traits. Moderate direct heritabilities (ha2) for BW, WW, ADW, and KR were found to be as 0.18 ± 0.03, 0.50 ± 0.04, 0.47 ± 0.04, and 0.37 ± 0.05, respectively. The proportion of maternal permanent environmental effect (c2) to the total phenotypic variance (σ2p) was estimated as 0.00 ± 0.00, 0.12 ± 0.02, 0.11 ± 0.02, and 0.18 ± 0.03 for BW, WW, ADWG, and KR, respectively. The genetic, phenotypic, and environmental correlations between the pre-weaning growth traits were found to be ranging from - 0.02 to 0.99. Thus, our study suggests moderate heritabilities and positive and relatively high genetic correlations among the observed pre-weaning growth traits. These results have implications in terms of providing rapid genetic progress for these traits in breeding programs of Kilis goats.

Visual processing deficits in patients with schizophrenia spectrum and bipolar disorders and associations with psychotic symptoms, and intellectual abilities

ObjectiveLow-level sensory disruption is hypothesized as a precursor to clinical and cognitive symptoms in severe mental disorders. We compared visual discrimination performance in patients with schizophrenia spectrum disorder or bipolar disorder with healthy controls, and investigated associations with clinical symptoms and IQ. MethodsPatients with schizophrenia spectrum disorder (n = 32), bipolar disorder (n = 55) and healthy controls (n = 152) completed a computerized visual discrimination task. Participants responded whether the latter of two consecutive grids had higher or lower spatial frequency, and discrimination thresholds were estimated using an adaptive maximum likelihood procedure. Case-control differences in threshold were assessed using linear regression, F-test and post-hoc pair-wise comparisons. Linear models were used to test for associations between visual discrimination threshold and psychotic symptoms derived from the PANSS and IQ assessed using the Matrix Reasoning and Vocabulary subtests from the Wechsler Abbreviated Scale of Intelligence (WASI). ResultsRobust regression revealed a significant main effect of diagnosis on discrimination threshold (robust F = 6.76, p = .001). Post-hoc comparisons revealed that patients with a schizophrenia spectrum disorder (mean = 14%, SD = 0.08) had higher thresholds compared to healthy controls (mean = 10.8%, SD = 0.07, β = 0.35, t = 3.4, p = .002), as did patients with bipolar disorder (12.23%, SD = 0.07, β = 0.21, t = 2.42, p = .04). There was no significant difference between bipolar disorder and schizophrenia (β = −0.14, t = −1.2, p = .45). Linear models revealed negative associations between IQ and threshold across all participants when controlling for diagnostic group (β = −0.3, t = −3.43, p = .0007). This association was found within healthy controls (t = −3.72, p = .0003) and patients with bipolar disorder (t = −2.53, p = .015), and no significant group by IQ interaction on threshold (F = 0.044, p = .97). There were no significant associations between PANSS domain scores and discrimination threshold. ConclusionPatients with schizophrenia spectrum or bipolar disorders exhibited higher visual discrimination thresholds than healthy controls, supporting early visual deficits among patients with severe mental illness. Discrimination threshold was negatively associated with IQ among healthy controls and bipolar disorder patients. These findings elucidate perception-related disease mechanisms in severe mental illness, which warrants replication in independent samples.