Parameter Estimation Research Articles

Locally sparse and robust partial least squares in scalar-on-function regression

We present a novel approach for estimating a scalar-on-function regression model, leveraging a functional partial least squares methodology. Our proposed method involves computing the functional partial least squares components through sparse partial robust M regression, facilitating robust and locally sparse estimations of the regression coefficient function. This strategy delivers a robust decomposition for the functional predictor and regression coefficient functions. After the decomposition, model parameters are estimated using a weighted loss function, incorporating robustness through iterative reweighting of the partial least squares components. The robust decomposition feature of our proposed method enables the robust estimation of model parameters in the scalar-on-function regression model, ensuring reliable predictions in the presence of outliers and leverage points. Moreover, it accurately identifies zero and nonzero sub-regions where the slope function is estimated, even in the presence of outliers and leverage points. We assess our proposed method’s estimation and predictive performance through a series of Monte Carlo experiments and an empirical dataset—that is, data collected in relation to oriented strand board. Compared to existing methods our proposed method performs favorably. Notably, our robust procedure exhibits superior performance in the presence of outliers while maintaining competitiveness in their absence. Our method has been implemented in the robsfplsr package in .

Advanced extraction of PV parameters’ models based on electric field impacts on semiconductor conductivity using QIO algorithm

This article presents a novel approach for parameters estimation of photovoltaic cells/modules using a recent optimization algorithm called quadratic interpolation optimization algorithm (QIOA). The proposed formula is dependent on variable voltage resistances (VVR) implementation of the series and shunt resistances. The variable resistances reduced from the effect of the electric field on the semiconductor conductivity should be included to get more accurate representation. Minimizing the mean root square error (MRSE) between the measured (I–V) dataset and the extracted (V–I) curve from the proposed electrical model is the main goal of the current optimization problem. The unknown parameters of the proposed PV models under the considered operating conditions are identified and optimally extracted using the proposed QIOA. Two distinct PV types are employed with normal and low radiation conditions. The VVR TDM is proposed for (R.T.C. France) silicon PV operating at normal radiation, and eleven unknown parameters are optimized. Additionally, twelve unknown parameters are optimized for a Q6-1380 multi-crystalline silicon (MCS) (area 7.7 cm2) operating under low radiation. The efficacy of the QIOA is demonstrated through comparison with four established optimizers: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Sine Cosine Algorithm (SCA). The proposed QIO method achieves the lowest absolute current error values in both cases, highlighting its superiority and efficiency in extracting optimal parameters for both Single-Crystalline Silicon (SCS) and MCS cells under varying irradiance levels. Furthermore, simulation results emphasize the effectiveness of QIO compared to other algorithms in terms of convergence speed and robustness, making it a promising tool for accurate and efficient PV parameter estimation.

Relative Entropy of Coherence Quantifies Performance in Bayesian Metrology

The ability of quantum states to be in superposition is one of the key features that sets them apart from the classical world. This “coherence” is rigorously quantified by resource theories, which aim to understand how such properties may be exploited in quantum technologies. There has been much research on what the resource theory of coherence can reveal about quantum metrology, almost all of which has been from the viewpoint of Fisher information. We prove, however, that the relative entropy of coherence, and its recent generalization to positive operator-valued measures (POVMs), naturally quantify the performance of Bayesian metrology. In particular, we show how a coherence measure can be applied to an ensemble of states. We then prove that during parameter estimation, the ensemble relative entropy of coherence (C) is equal to the difference between the optimal Holevo information (X), and the mutual information attained by a measurement (I). We call this relation the CXI equality. The ensemble coherence lets us visualize how much information is locked away in superposition and hence is inaccessible with a given measurement scheme and quantifies the advantage that would be gained by using a joint measurement on multiple states. Our results hold regardless of how the parameter is encoded in the state, encompassing unitary, dissipative, and discrete settings. We consider both projective measurements and general POVMs. This work suggests new directions for research in coherence, provides a novel operation interpretation for the relative entropy of coherence and its POVM generalization, and introduces a new tool to study the role of quantum features in metrology. Published by the American Physical Society 2024

Learning mathematics with personal mobile devices in school: a multigroup invariance analysis of acceptance among students and teachers

Students’ use of personal mobile devices (PMDs), including smartphones, tablets, and laptops, in mathematics classrooms globally has become feasible due to the implementation of the bring your own device (BYOD) initiatives and policies in schools. Students’ academic use of PMDs in mathematics learning has been associated with increased motivation, enjoyment, and enhanced performance. While debates continue worldwide on the bans of PMDs in schools, it is noteworthy that students in Namibian basic education are not permitted to use PMDs in schools. This study employed a comparative nonexperimental quantitative research design to assess the perceived usefulness (PU) and behavioural intention (BI) to use PMDs in mathematics classrooms among 500 Namibian students and 209 teachers. The study employed a volunteer purposeful sampling method to select participating students and teachers from government schools in the Omusati and Khomas regions. Student selection was based on self-selection from full-class mathematics groups. Similarly, the study utilised volunteer purposeful sampling to recruit grades 4–12 mathematics teachers. Utilising structural equation modelling, the findings revealed that students expressed more positive ratings for PMD usefulness and intention than did teachers. Furthermore, PU significantly predicted students’ BI to learn mathematics with PMDs in school. Multigroup confirmatory factor analysis supported configural, metric, and scalar invariance in PU and BI factor structures across both participant groups. However, when students’ latent means of PU and BI are set to 0, the teachers’ latent mean parameter estimates are negative, suggesting potential disparities. Teachers should recognise and leverage students’ positive perceptions of PMDs to enhance motivation and engagement in mathematics learning. Simultaneously, ensuring consistent assessment across participant groups is essential, while addressing teachers’ PMDs reservations through professional development and support remains crucial.

Group inference of high-dimensional single-index models

For the supervised and semi-supervised settings, a group inference method is proposed for regression parameters in high-dimensional semi-parametric single-index models with an unknown random link function. The inference procedure is based on least squares, which can be extended to other general convex loss functions. The proposed test statistics are weighted quadratic forms of the regression parameter estimates, in which the weight could be a non-random matrix or the sample covariance matrix of the covariates. The proposed method could detect dense but weak signals and deal with high correlation of covariates inside the group. A ‘contaminated test statistic’ is established in the semi-supervised regime to decrease the variance. The asymptotic properties of the resulting estimators are established. The finite-sample behaviour of the proposed method is evaluated through extensive simulation studies. Applications to two genomic datasets are provided.

A New Look at the Dirichlet Distribution: Robustness, Clustering, and Both Together

AbstractCompositional data have peculiar characteristics that pose significant challenges to traditional statistical methods and models. Within this framework, we use a convenient mode parametrized Dirichlet distribution across multiple fields of statistics. In particular, we propose finite mixtures of unimodal Dirichlet (UD) distributions for model-based clustering and classification. Then, we introduce the contaminated UD (CUD) distribution, a heavy-tailed generalization of the UD distribution that allows for a more flexible tail behavior in the presence of atypical observations. Thirdly, we propose finite mixtures of CUD distributions to jointly account for the presence of clusters and atypical points in the data. Parameter estimation is carried out by directly maximizing the maximum likelihood or by using an expectation-maximization (EM) algorithm. Two analyses are conducted on simulated data to illustrate the effects of atypical observations on parameter estimation and data classification, and how our proposals address both aspects. Furthermore, two real datasets are investigated and the results obtained via our models are discussed.

Solutions of crack constraint parameter in 3D models −− II: Through T-stress

To more accurately analyze the crack front fields, especially under the low constraint conditions which are more common in practical engineering structures, two-parameter approaches (e.g. J-A(A2), J-Q, etc.) are proposed. Currently, no theoretical solutions of the constraint parameters (e.g. A(A2), Q, etc.) in two-parameter approaches are available. Meanwhile, numerical methods for constraint parameter determination are not practical because of significant resource and time consumption, especially for 3D solid models or engineering structures. Therefore, theory and formulae for the estimation of constraint parameter A in 3D solid models are developed in the current work based on T-stress, so as to make it possible to apply two-parameter approaches in practical engineering structures under normal working condition. Two actual structures are used to validate the engineering application of the developed estimate method and formulae.

Comparação das distribuições Normal, Logística, Laplace e t de Student para o erro experimental na descrição bayesiana do acúmulo de matéria seca de Allium sativum

ABSTRACT: This study assessed distributions associated with Bayesian nonlinear modeling error in the description of total plant dry matteraccumulation (TDMA) of Allium sativumas a function of days after planting (DAP). According to the DIC criterion, Logistic and Gompertzmodels that use student’s t distribution error exhibited the highest DIC with logistic error distribution. In general, the difference of DIC in all the scenarios was not more than 5.The Bayes factor (BF) criterion showed no difference in the Logistic and Gompertzmodel fit when four distributions are used for the errors, where BF values do not exceed 2. Posterior distributions and the usual estimators of Logistic and Gompertz model parameters were similar even forvaried error distribution. In summary, there was no difference in the use of 4 distributions associated with the modeling error of garlic plant growth by the Bayes factor, whereby the results showed that alternating between error distributions significantly changes the number of Markov Chain Monte Carlo (MCMC) iterations.

Estimation of some AM2 model parameters from a derived empirical logistic function of methane production

Because of its capability to convert organic wastes into renewable energy and into some components useful for agriculture, the anaerobic digestion technology can reduce greenhouse gas emissions in the atmosphere and the pollution. Thus, anaerobic digestion can contribute to achieving some of sustainable development goals. Consequently, many theoretical and empirical approaches are proposed for estimating, predicting and optimizing the methane produced by anaerobic digestion. In this context, the logistic function is a mathematical model that can be used to approximate empirical data of the temporal methane production in anaerobic digestion. In a previous paper, under some appropriate approximations, we have derived from AM2 model a single analytical expression in a form of a logistic function for describing the evolution of methane production in batch bioreactors. In the present paper, by comparing the three standard parameters associated with the classical empirical logistic function with that of the derived one from AM2 model; some relationships between them have been established. These relations are exploited for estimating some coefficients and parameters of AM2 model with respect to empiric logistic function parameters and vice-versa. Moreover, this possibility enables more qualitative insight about the evolution of the methane production and the influence of AM2 parameters and coefficients as well as their interaction over its processes.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National ToxicologyProgram.

A whale optimization algorithm-based multivariate exponential smoothing grey-holt model for electricity price forecasting

Accurately forecasting electricity prices is essential for a variety of stakeholders in the energy sector, including market investors, policymakers, and consumers. However, existing forecasting techniques are often limited by complex parametric estimates and strict restrictions on input variables. This paper proposes a Whale Optimization Algorithm (WOA)-based multivariate exponential smoothing Grey-Holt (GMHES) model for electricity price forecasting. The proposed WOA-GMHES(1,N) model uses historical data to learn the underlying trends and patterns of electricity prices. The WOA algorithm is used to optimize the model parameters, which are adaptively adjusted to reflect the changing dynamics of the electricity market. The proposed model is evaluated on real high- and low-voltage electricity price data from Cameroon. The results show that the novel WOA-GMHES(1,N) model outperforms competing models, achieving RMSE and SMAPE scores of 0.1359 and 0.61%, respectively. This novel model is also computationally efficient, requiring less than 1.3 s to generate a forecast. The proposed WOA-GMHES(1,N) model is a promising novel approach for electricity price forecasting. The model is accurate, efficient, and flexible, making it a valuable tool for a variety of stakeholders in the energy sector.

GIS-BASED ESTIMATION OF SHORELINE CHANGE AT THE OLIE PIER HARBOR HERITAGE SITE, MANGGAR, EAST BELITUNG, INDONESIA

The coastal area is a dynamic region influenced by continuous interactions between land and the sea. Changes in coastal compositions are closely related to shoreline instability. This study analyzes coastline changes at the Olie Pier Heritage Site, Manggar, East Belitung, Indonesia, using statistical-based techniques EPR (End Point Rate) and NSM (Net Shoreline Movement) 2015 to 2023. The DSAS (Digital Shoreline Analysis System) was employed to calculate the shoreline alterations in every transect with a distance of about 100 m. The study area is generally predominated by a moderate abrasion. The EPR and NSM values indicate a potential for future shoreline changes, considering the present status and future estimations of oceanographic parameters (currents, wind, waves, tides). The highest erosion is identified in the Lalang Village with a shoreline retreat (NSM) of -65.38 m and a retreat rate (EPR) of 8.78 m/year. On the other hand, the highest accretion is observed in the Baru Village with a NSM of -56.68 m and EPR of 7.61 m/year. The implications of these shoreline changes on the heritage site and the coastal environment contribute to the management of coastal areas amidst global environmental challenges. This study provides valuable insights into conservation and sustainable development efforts in the coastal region of East Belitung.

Inversion strategies for Q estimation in viscoacoustic full-waveform inversion

Estimation of an attenuation parameter, represented by the quality factor Q, holds paramount importance in seismic exploration. One of the main challenges in Q estimation through viscoacoustic full-waveform inversion (FWI) is effectively decoupling Q from velocity. In this study, our objective is to enhance Q inversion by addressing critical aspects, such as gradient preconditioning, workflow, and misfit design. By developing a new preconditioner that approximates the diagonal of the Hessian, we facilitate automatic parameter tuning across different classes, ensuring comparable magnitudes of preconditioned gradients for velocity and Q. Moreover, our investigations confirm the efficacy of the two-stage hierarchical strategy in mitigating velocity- Q trade-offs, enabling more accurate Q estimation by first focusing on velocity reconstruction before jointly estimating velocity and Q. The analysis and numerical examples also highlight the importance of broadband data and long-offset acquisition for a reliable Q estimation. In addition, leveraging amplitude information can improve Q estimation to some extent, but careful consideration of frequency band and noise effects is necessary. We explore two misfit functions that capture amplitude variation with frequency in the time-frequency domain, noting their sensitivity to noise. To address this, we develop a differential strategy that can effectively mitigate the effects of low-frequency noise. This comprehensive study on enhancing Q estimation in viscoacoustic FWI offers valuable insights for multiparameter inversion in realistic scenarios.

Population pharmacokinetics of Ainuovirine and exposure-response analysis in human immunodeficiency virus-infected individuals.

Ainuovirine (ANV) is a new generation of non-nucleoside reverse transcriptase inhibitor for the treatment of human immunodeficiency virus (HIV) type 1 infection. This study aimed to evaluate the population pharmacokinetic (PopPK) profile and exposure-response relationship of ANV among people living with HIV. Plasma concentration-time data from phase 1 and phase 3 clinical trials of ANV were pooled for developing the PopPK model. Exposure estimates obtained from the final model were used in exposure-response analysis for virologic responses and safety responses. ANV exhibited a nonlinear pharmacokinetic profile, which was best described by a two-compartment model with first-order elimination. There were no significant covariates correlated to the pharmacokinetic parameters of ANV. The PopPK parameter estimate (relative standard error [%]) for CL/F was 6.46 (15.00) L/h, and the clearance of ANV increased after multiple doses. The exposure-response model revealed no significant correlation between the virologic response (HIV-RNA <50 copies/mL) at 48 weeks and the exposure, but the incidence of adverse events increased with the increasing exposure( P value of steady-state trough concentration and area under the steady-state curve were 0.0177 and 0.0141, respectively). Our PopPK model supported ANV 150mg once daily as the recommended dose for people living with HIV, requiring no dose adjustment for the studied factors. Optimization of ANV dose may be warranted in clinical practice due to an increasing trend in adverse reactions with increasing exposure. Chinese Clinical Trial Registry https://www.chictr.org.cn (Nos. ChiCTR1800018022 and ChiCTR1800019041).

Quartic Transmuted Exponential Distribution: Characteristics and Parameter Estimation

Quartic Transmuted Exponential Distribution: Characteristics and Parameter Estimation

Parameter estimation of Chen distribution under improved adaptive type-II progressive censoring

Parameter estimation of Chen distribution under improved adaptive type-II progressive censoring

Quantitative Stylistic Analysis of Middle Chinese Texts Based on the Dissimilarity of Evolutive Core Word Usage

Stylistic analysis enables open-ended and exploratory observation of languages. To fill the gap in the quantitative analysis of the stylistic systems of Middle Chinese, we construct lexical features based on the evolutive core word usage and scheme a Bayesian method for feature parameters estimation. The lexical features are from the Swadesh list, each of which has different word forms along with the language evolution during the Middle Ages. We thus count the varied word of those entries along with the language evolution as the linguistic features. With the Bayesian formulation, the feature parameters are estimated to construct a high-dimensional random feature vector to obtain the pair-wise dissimilarity matrix of all the texts based on different distance measures. Finally, we perform the spectral embedding and clustering to visualize, categorize, and analyze the linguistic styles of Middle Chinese texts. The quantitative result agrees with the existing qualitative conclusions and, furthermore, betters our understanding of the linguistic styles of Middle Chinese from both the inter-category and intra-category aspects. It also helps unveil the special styles induced by the indirect language contact.

Dose rationale for the use of meropenem/vaborbactam combination in paediatric patients with Gram-negative bacterial infections.

Meropenem/vaborbactam combination is approved in adults by FDA and EMA for complicated urinary tract infections and by EMA also for other Gram-negative infections. We aimed to characterise the pharmacokinetics of both moieties in an ongoing study in children and use a model-based approach to inform adequate dosing regimens in paediatric patients. Over 4196 blood samples of meropenem and vaborbactam (n = 414 subjects) in adults, together with 114 blood samples (n = 39) in paediatric patients aged 3 months to 18 years were available for this analysis. Data were analysed using a population with prior information from a pharmacokinetic model in adults to inform parameter estimation in children. Simulations were performed to assess the suitability of different dosing regimens to achieve adequate probability of target attainment (PTA). Meropenem/vaborbactam PK was described with two-compartment models with first-order elimination. Body weight and CLcr were significant covariates on the disposition of both drugs. A maturation function was evaluated to explore changes in clearance in neonates. PTA ≥90% was derived for children aged ≥3 months after 3.5-h IV infusion of 40 mg/kg Q8h of both meropenem and vaborbactam and 2 g/2 g for those ≥50 kg. Extrapolation of disposition parameters suggest that adequate PTA is achieved after a 3.5-h IV infusion of 20 mg/kg for neonates and infants (3 months). An integrated analysis of adult and paediatric data allowed accurate description of sparsely sampled meropenem/vaborbactam PK in paediatric patients and provided recommendations for the dosing in neonates and infants (3 months).

Shadow Economy: Determinants and Its Impact on Foreign Direct Investment

The shadow economy poses a significant threat to government revenue and the effectiveness of economic policies. This paper investigates the causes of the shadow economy and its influence on foreign direct investment (FDI). Our study employs the currency demand approach, a component of the indirect method, to identify the determinants of the shadow economy in a dataset covering 105 countries from 2001 to 2017. These countries are categorized into four income groups: high-income, upper-middle-income, lower-middle-income, and low-income. Parameter estimation is conducted using the Generalized Method of Moments (GMM) model, with robustness tests incorporating reference estimates from Partial Least Squares (PLS) and Fixed Effects Model (FEM). Our findings indicate that a higher GDP and lower interest rates are associated with reduced shadow economy activity. Elevated market interest rates increase the cost of funds in the informal sector, discouraging engagement in shadow economic activities due to reduced profitability. Furthermore, higher tax revenues correlate with intensified regulatory enforcement, increasing the risks associated with shadow economy involvement. A larger workforce and lower unemployment rates similarly diminish shadow economy activity. In the context of foreign direct investment (FDI), the shadow economy positively affects FDI flows when formal institutions, including legal frameworks, property rights protection, and regulatory systems, are either weak or overly burdensome. In such scenarios, economic actors may opt for informal channels like the shadow economy, offering a flexible and cost-effective alternative to the formal sector, a crucial consideration for foreign investors.

Parameter Estimation Method for Virtual Power Plant Frequency Response Model Based on SLP

In adapting to the double-high development trend of high-voltage direct current (HVDC) receiving-end power systems and solving optimization problems in emergency frequency control (EFC) supporting virtual power plants (VPPs) in large-scale power systems, a parameter estimation method for a VPP frequency response model based on a successive linear programming (SLP) method is proposed. First, a “centralized/decentralized” hierarchical control architecture for VPP participation in EFC is designed. Second, the frequency response characteristics of multiple flexible resources are scientifically analyzed, and the system frequency response (SFR) model and equivalent model of VPP are constructed. Subsequently, parameter estimation of the VPP frequency response model is carried out based on the SLP method, aiming to balance the accuracy and computational efficiency of the model. Finally, the effectiveness of the proposed methodology is verified by using PSD-BPA to simulate and test the three-zone HVDC recipient area grid.