Comparison of confirmatory factor analysis estimation methods on mixed-format data

Weibull distribution is widely used in various areas such as life tables, failure rates, and definition of wind speed distribution. Therefore, parameter estimation for the Weibull distribution is an important problem in many real data applications. The least square (LS), the weighted least square (WLS) and the maximum likelihood (ML) are the most popular methods for the parameter estimation in the Weibull distribution. In this study, based on the LS, WLS and ML estimation methods, a multi-objective programming approach is proposed for the parameter estimation of two-parameter Weibull distribution. This new approach evaluates together LS, WLS and ML methods in the estimation process. NSGA-II method, which is a multi-objective heuristic optimization method, is used to solve the proposed multi-objective estimation model. To evaluate the applicability and performance of the proposed approach, a detailed Monte Carlo simulation study based on deficiency criteria and a real data application are designed. The results illustrated that the proposed multi-objective programming approach provides quite accurate parameter estimates for the two parameter Weibull distribution with respect to deficiency criteria.

A great obstacle for wider use of structural equation modeling (SEM) has been the difficulty in handling categorical variables. Two data sets with known structure between 2 related binary outcomes and 4 independent binary variables were generated. Four SEM strategies and resulting apparent validity were tested: robust maximum likelihood (ML), tetrachoric correlation matrix input followed by SEM ML analysis, SEM ML estimation for the sum of squares and cross-products (SSCP) matrix input obtained by the log-linear model that treated all variables as dependent, and asymptotic distribution-free (ADF) SEM estimation. SEM based on the SSCP matrix obtained by the log-linear model and SEM using robust ML estimation correctly identified the structural relation between the variables. SEM using ADF added an extra parameter. SEM based on tetrachoric correlation input did not specify the data generating process correctly. Apparent validity was similar for all models presented. Data transformation used in log-linear modeling can serve as an input for SEM.

We revisit the problem of blind calibration of uniform linear sensors arrays for narrowband signals and set the premises for the derivation of the optimal blind calibration scheme. In particular, instead of taking the direct (rather involved) Maximum Likelihood (ML) approach for joint estimation of all the unknown model parameters, we follow Paulraj and Kailath’s classical approach in exploiting the special (Toeplitz) structure of the observed covariance. However, we offer a substantial improvement over Paulraj and Kailath’s Least Squares (LS) estimate by using asymptotic approximations in order to obtain simple, (quasi-)linear Weighted LS (WLS) estimates of the sensors’ gains and phases offsets with asymptotically optimal weighting. As we show in simulation experiments, our WLS estimates exhibit near-optimal performance, with a considerable improvement (reaching an order of magnitude and more) in the resulting mean squared errors, w.r.t. the corresponding ordinary LS estimates. We also briefly explain how the methodology derived in this work may be utilized in order to obtain (by certain modifications) the asymptotically optimal ML estimates w.r.t. the raw data via a (quasi)-linear WLS estimate.

Identity is a powerful concept that influences behavior and health. For over thirty years, researchers in sport psychology have been using the Athletic Identity Measurement Scale (AIMS) as a research instrument providing insights into the relationships between athletic identity and health variables. While musician identity is recognized as an important factor to be investigated in relation to occupational health, there are no known robust instruments like the AIMS in music psychology research. The current study aimed to adapt and validate the athletic identity measurement scale for use with musicians. The AIMS history includes episodes of modifications for performance enhancement of the instrument that resulted in five different models. The validation process includes evaluating the psychometrical properties across all five models. The sample included student musicians and non-student musicians (N = 1040). The traditional confirmatory factor analysis (CFA) and the maximum likelihood (ML) estimation method were used. The exploratory structural equation modeling (ESEM) and robust weighted least squares (WLS) was utilized to explore a new method of estimation that was known to resolve issues consistent with the CFA and ML method. The goodness-of-fit indices of CFA and ESEM were compared. The results showed that the MIMS is a reliable and valid instrument for use with musicians. The factor structure and the goodness-of fit-indices were discussed across all the models of MIMS.

This paper deals with study and estimation of stress-strength reliability for a system where strength and stress components are connected in series with cold standby redundancy at system level. It is supposed that the random stress and strength both follow Kumaraswamy half-logistic distribution. In this redundant system, we consider that there N subsystems and in each subsystem, there are M statistically independent strength components under the impact of M statistically independent stress components The problem of estimation is solved in two cases. First under the assumption that random stress and strength have common first shape parameter and different second shape parameter and second with the assumption that common shape parameter is known. The stress-strength reliability is estimated using maximum likelihood and Bayesian estimation methods. Also asymptotic and Bayesian intervals for the stress-strength reliability under both the cases are constructed. Monte Carlo simulations will be performed to compare the performance of various methods. Finally a real life data set is analyzed to demonstrate the findings.

This study aims to compare the efficacy of logistic regression model for identifying the risk factors of low-birth-weight babies in Indonesia using the maximum likelihood estimation (MLE)and the Bayesian estimation methods. The data used in this study is secondary data derived from the 2017 Indonesian Demographic Health Survey with a total sample of 16,344 newborn babies. Selection of the best logistic regression model was based on the smaller Bayesian Schwartz Information Criterion (BIC) value. The logistic regression model with the Bayesian estimation method has a smaller BIC value than the MLE method. Twin births, baby girl, maternal age at risk, birth spacing that is too close, iron deficiency, low education, low economy, inadequate drinking water sources have provided a higher risk of low-birth-weight incidence.

This systematic review and meta-analysis aimed to compare the efficacy of intralesional platelet-rich plasma and intralesional steroids in managing oral lichen planus, focusing on pain and lesion size reduction. A systematic search of MEDLINE (via PubMed), Web of Science, Scopus, and Embase was conducted up to May 30, 2024, to identify randomized controlled trials (RCTs) comparing the efficacy of intralesional platelet-rich plasma (PRP) and intralesional steroids in managing oral lichen planus (OLP). The study adhered to a predefined PICOS framework: (P) adults diagnosed with OLP, (I) PRP application, (C) steroid application, (O) outcomes related to pain reduction and lesion size, and (S) RCTs. The analysis utilized both random-effects and Weighted Least Squares (WLS) models to account for heterogeneity and assess publication bias. Seven articles met the eligibility criteria, of which six were included in the meta-analysis, encompassing 141 participants. While the analysis was limited by a small sample size, significant heterogeneity, and a lack of long-term follow-up, analysis with random-effects and WLS model provided a robust comparative evaluation of PRP and steroids for OLP treatment. Pain Reduction: The pooled effect size was -0.34 (95% CI: -0.94 to 0.27; p = 0.274), indicating a moderate, non-significant preference for steroids over PRP. Moderate to high heterogeneity (I² = 68.516%) suggested substantial variability in effect sizes. Lesion Size Reduction: The pooled effect size was -0.10 (95% CI: -0.58 to 0.39; p = 0.697), favoring PRP slightly but without statistical significance. Low to moderate heterogeneity (I² = 31.42%) was observed. PRP demonstrated comparable efficacy to steroids in reducing pain and lesion size in OLP patients, with the added advantage of fewer side effects. However, significant heterogeneity and publication bias necessitate further high-quality research to substantiate these findings.

A method of modeling slowly time-varying binary switching elements with a periodic two-state semi-Markovian parametric model is presented. An optimal Maximum Likelihood (ML) estimator is derived for general holding time distributions. In addition, a slight restriction on the holding time distribution reduces the ML estimator into a Weighted Least Square (WLS) estimator. Finally, a linearization of the WLS equation results in a sub-optimal Linear Least Square (LLS) estimator which is non-iterative and involves finding the solution to a set of matrix equations, where the matrices are correlation matrices.

Asymptotically efficient GNSS trilateration

Considering the impact of the heterogeneous conditions of the mixture baseline distribution on the parameter estimation of a composite dynamical system (CDS), we propose an approach to infer the model parameters and baseline survival function of CDS using the maximum likelihood estimation and Bayesian estimation methods. The power-trend hazard rate function and Burr type XII mixture distribution as the baseline distribution are used to characterize the changes of the residual lifetime distribution of surviving components. The Markov chain Monte Carlo approach via using a new Metropolis–Hastings within the Gibbs sampling algorithm is proposed to overcome the computation complexity when obtaining the Bayes estimates of model parameters. A numerical example is generated from the proposed CDS to analyze the proposed procedure. Monte Carlo simulations are conducted to investigate the performance of the proposed methods, and results show that the proposed Bayesian estimation method outperforms the maximum likelihood estimation method to obtain reliable estimates of the model parameters and baseline survival function in terms of the bias and mean square error.

In experimental design applications unbiased estimators si 2 of the variances σi 2 are possible. These estimators may be used in Weighted Least Squares (WLS) when estimating the parameters β. The resulting small-sample behavior is investigated in a Monte Carlo experiment. This experiment shows that an asymptotically valid covariance formula can be used if si 2 is based on, say, at least 5 observations. The WLS estimator based on estimators si 2 gives more accurate estimators of β, provided the σi 2 differ by a factor, say, 10. Keywords: generalized least squaresexperimental errorMonte Carlo

We study geolocation based on biased range estimates. Positive bias arises using time delay ranging methods in a multipath fading environment, when the line of sight direct path is severely attenuated. We model the range measurement as contaminated with Gaussian noise and an additive nonnegative bias term, and consider deterministic and random bias cases. We develop weighted least squares (WLS) and maximum likelihood (ML) geolocation estimators, and show that in general they yield biased geolocation estimates. A perturbation analysis technique is applied to find bias and mean square error (MSE) expressions for the WLS and MLE algorithms. MLE generally outperforms WLS, because MLE exploits knowledge of the range measurement bias distribution. The location error expressions are functions of the measurement bias and variance, as well as the network geometry. These results are useful to study achievable geolocation performance, and are applied to optimize sensor placement for improving the overall geolocation accuracy. We also develop the Cramér-Rao bound (CRB) on geolocation for our model. The CRB is a bound on unbiased estimation, whereas the geolocation algorithms may be biased, and we show how the estimators approach the CRB in certain cases. Numerical examples are presented to verify the analysis and study some cases of interest.

The “covariation method” for estimating the parameters of the standard Dynamic Energy Budget model II: Properties and preliminary patterns

It is known that various types of location privacy attacks can be carried out using a personalized transition matrix that is learned for each target user, or a population transition matrix that is common to all target users. However, since many users disclose only a small amount of location information in their daily lives, the training data can be extremely sparse. The aim of this paper is to clarify the risk of location privacy attacks in this realistic situation. To achieve this aim, we propose a learning method that uses tensor factorization (or matrix factorization) to accurately estimate personalized transition matrices (or a population transition matrix) from a small amount of training data. To avoid the difficulty in directly factorizing the personalized transition matrices (or population transition matrix), our learning method first factorizes a transition count tensor (or matrix), whose elements are the number of transition counts that the user has made, and then normalizes counts to probabilities. We focus on a localization attack, which derives an actual location of a user at a given time instant from an obfuscated trace, and compare our learning method with the maximum likelihood (ML) estimation method in both the personalized matrix mode and the population matrix mode. The experimental results using four real data sets show that the ML estimation method performs only as well as a random guess in many cases, while our learning method significantly outperforms the ML estimation method in all of the four data sets.

In this paper, a constrained weighted least squares (CWLS) problem with two quadratic constraints for hybrid AOA, TDOA, and RSS localization is formulated. The Lagrange multiplier method cannot be efficiently used in this situation, so we provide four methods to solve the CWLS problem: 1) weighted least squares (WLS); 2) iterative constrained weighted least squares (ICWLS); 3) Lagrange multiplier method combined with Newton’s method (LMM+NM); 4) semidefinite programming (SDP). Furthermore, in order to obtain a good balance between accuracy and computational burden, we develop a fixed point iteration (FPI)-based algorithm that is derived from an approximate maximum likelihood estimator (MLE). Simulation results show that the accuracy of the proposed FPI method is better than the other four methods; besides, its computational burden is one in ten of the SDP algorithm.