Parameter Estimation Research Articles

MODELS FOR DESCRIBING THE VARIATION IN GRAIN WEIGHT IN WHEAT EARS – ANALYSIS OF THE VENEZIO VARIETY

The study analyzed the productivity elements of the wheat ear, the Venezio variety, and used regression analysis to model the variation of grain weight in the ear in relation to the morphological productivity elements of the ear. The experiment was carried out within the ULS “King Mihai I” from Timisoara, during 2021 - 2022. The ear productivity elements determined were: ear length (EL), spikelet number per ear (SpkN), grain number per ear (GNE), and grain weight per ear (GWE). The experimental data showed statistical reliability (F>Fcrit, Alpha = 0.001). A strong, positive correlation was recorded between GNE and GWE (r = 0.89). Moderate correlation was recorded between SpkN and GNE (r = 0.70). Low correlation was recorded between SpkN and GWE (r = 0.63), between EL and SpkN (r = 0.57) and between EL and GWE (r = 0.52), respectively. Regression analysis facilitated the estimation of the GWE parameter under different conditions of statistical certainty; R2 = 0.604, p = 0.089 when using the EL and SpkN parameters, R2 = 0.892, p<0.001 when using the EL and GNE parameters, respectively R2 = 0.817, p = 0.0038 when using the SpkN and GNE parameters. The RMSEP parameter confirmed the results of estimating the GWE, respectively RMSEP = 0.1601 in the case of estimating GWE based on EL and GNE. The results recorded, under the study conditions, are important to quantify the grain yield of the wheat ear, the Venezio variety, and to analyze the technology elements and environmental factors that contributed to the variation of productivity elements and the formation of GWE, at the level of wheat ears.

Structural Change and Diagnosis of Nepalese Stock Market Volatility

Purpose: This paper studies the volatility of the Nepalese stock market. It focuses on two aspects of volatility: persistence and leverage effects. It aims to evaluate whether the presence of deterministic structural shifts in volatility produces biased estimations of persistence and leverage parameters. Design/Methodology: This study uses time-series econometric modeling. The family of asymmetric GARCH specifications has been used to capture the leverage effect and persistence in the longitudinal univariate time-series indices data. The study covers 13 subindices along with a composite NEPSE index covering the period from 2003 to 2023. Findings: Findings suggested that the estimates of persistence and leverage parameters are biased when deterministic structural shifts are not considered. Result indicated the downward adjustment of the estimated parameters of leverage and persistence when such structural shifts are incorporated in the models. Conclusions: Failure to incorporate deterministic structural shifts are expected to produce biased estimate of volatility attributes of high-frequency financial time series data. A downward shift in persistence parameters indicates that the impact of recent lagged conditional variance and information shocks have larger effect on expected volatility compared to that of distant conditiona variances. Decrease in leverage parameters in the presence of deterministic structural shifts suggested the reduction in asymmetry of the news impact (‘bad news’ vs ‘good news’). Implications: Investors and researchers when analyzing the high-frequency univariate financial time-series shall incorporate deterministic structural shifts in their analysis to ensure more robust analysis. Understanding the adjusted persistence and leverage parameters allows decision-makers to make informed risk assessments and enables to recalibrate their strategies to respond more promptly to market changes.

ET Dra activity according to observations in 2018–2023

The results of new photometric observations of the chromospherically active star ET Dra, performed using telescopes of the Zvenigorod INASAN Observatory, the Russian-Cuban Observatory in the territory of the Republic of Cuba (Havana) and the Terskol INASAN Observatory (a total of 8 sets of observations), as well as the addition of archival observations of the Kamogata Wide-field Survey. According to the data received during 2018–2023 changes in the shape of the light curve caused by the rotational modulation of a star with spots on the surface, as well as studies of the long-term variability of the star’s brightness were investigated. Characteristic changes in the ET Dra light curve are noted including a decrease of the stellar brightness in V filter, its cyclic changes and subsequent brightness increase. The shape of the phase curve and the duration of the extended minimum of brightness vary. During the HJD 245 9670 – 245 9715 interval the amplitude of the star’s brightness variability reached a maximum value of more than 0.4 magnitude, as it was in 1990. For each of the 8 set of observations the surface temperature inhomogeneities map were calculated and estimates of the spotedness parameter S of the object were performed. Maximum value of parameter S was 32.2–33.5%. Based on the constructed power spectra the values of possible cycles of long-term activity of the star are obtained as 570 and 1160 days (1.56 and 3.18 years).

DOA estimation algorithm for non-circular signal by a large-spacing array with auxiliary element

In this paper, a direction of arrival (DOA) estimation algorithm for non-circular signal by a large-spacing uniform array with an auxiliary element has been presented. The auxiliary element is placed away from the last element of the large-spacing uniform array. The spacing between arbitrary two elements of the whole array is not limited by the half-wavelength of the signal. Firstly, two initial signal subspaces corresponding to the large-spacing uniform array and two row vectors corresponding to the auxiliary element are obtained by eigenvalue decomposition (EVD) of the correlation matrix. The two row vectors are expanded into two virtual signal subspaces by using the two initial signal subspaces. Then, the relationship between the virtual signal subspaces and the initial signal subspaces are used to form a high-dimensional signal subspace corresponding to a uniform linear array with half-wavelength spacing. Finally, DOA estimation can be got by using the estimation of signal parameters via the rotational invariance techniques (ESPRIT) algorithm. Compared with the traditional ESPRIT algorithm and non-circular ESPRIT (NC-ESPRIT) algorithm based on a conventional uniform linear array, the proposed algorithm has higher accuracy for DOA estimation and stronger robustness to the mutual coupling.

Minimum reduced-order models via causal inference

AbstractConstructing sparse, effective reduced-order models (ROMs) for high-dimensional dynamical data is an active area of research in applied sciences. In this work, we study an efficient approach to identifying such sparse ROMs using an information-theoretic indicator called causation entropy. Given a feature library of possible building block terms for the sought ROMs, the causation entropy ranks the importance of each term to the dynamics conveyed by the training data before a parameter estimation procedure is performed. It thus allows for an efficient construction of a hierarchy of ROMs with varying degrees of sparsity to effectively handle different tasks. This article examines the ability of the causation entropy to identify skillful sparse ROMs when a relatively high-dimensional ROM is required to emulate the dynamics conveyed by the training dataset. We demonstrate that a Gaussian approximation of the causation entropy still performs exceptionally well even in presence of highly non-Gaussian statistics. Such approximations provide an efficient way to access the otherwise hard to compute causation entropies when the selected feature library contains a large number of candidate functions. Besides recovering long-term statistics, we also demonstrate good performance of the obtained ROMs in recovering unobserved dynamics via data assimilation with partial observations, a test that has not been done before for causation-based ROMs of partial differential equations. The paradigmatic Kuramoto–Sivashinsky equation placed in a chaotic regime with highly skewed, multimodal statistics is utilized for these purposes.

Addressing life history information gaps for Caribbean parrotfishes: queen parrotfish Scarus vetula and stoplight parrotfish Sparisoma viride

Queen parrotfish Scarus vetula and stoplight parrotfish Sparisoma viride are widely distributed throughout the subtropical/tropical regions of the northwestern Atlantic, play important ecological roles in reef ecosystems, and contribute to small-scale commercial landings within several Caribbean management jurisdictions. Prior to this work, no comprehensive life history information existed for either species that combined otolith analysis and gonad histology. Queen parrotfish (n = 390) and stoplight parrotfish (n = 1801) were sampled throughout the U.S. Caribbean from 2013 to 2023. Queen parrotfish range in size from 82 to 402 mm FL and age from 0 to 16 years; stoplight parrotfish ranged from 73 to 433 mm FL and 0 to 20 years. Growth parameter estimates for queen parrotfish were L∞ = 347 mm FL and K = 0.42, when t0 was fixed to −0.06; for stoplight parrotfish, L∞ = 332 mm FL and K = 0.39, with a fixed t0 of −0.06. All female queen parrotfish transitioned to males by a maximum length and age of 322 mm FL and 14 years. In contrast, not all female stoplight parrotfish transitioned to males since the largest and oldest individuals sampled were females. Spawning capable queen parrotfish females were collected from November to August indicating a protracted spawning season of 10 months. Stoplight parrotfish exhibited year-round spawning with >50% of mature females in the spawning capable phase during all months of the year. Based on our overall findings related to life history, queen parrotfish and stoplight parrotfish in the U.S. Caribbean did not appear to exhibit signs of overexploitation which may in part relate to U.S. Caribbean management efforts currently in place that limit the minimum mesh size for traps (which ensures that smaller fish can escape from the traps), a ban on using gillnets to target parrotfish species, and the market driven targeting of “plate-size” fish by commercial spearfishers. The life history information documented in the current study will provide essential information for stock assessments and informed management in the U.S. Caribbean for these two important parrotfish species.

A Virtual Laboratory on Recursive Least-Squares Estimation for Undergraduate Courses

This paper describes the creation of an educational resource focused on identifying dynamic systems, with a particular emphasis on parametric estimation, for undergraduate courses. This virtual laboratory is easily reproducible with accessible materials and software, allowing easy system modification. The Recursive Least-Squares (RLS) with a forgetting factoris a solid and efficient method that enables students to experiment with parameters and better understand the subject matter. We show three experiments to provide an exhaustive RLScomprehension. Finally, we envision the virtual laboratory as useful for educational purposes and other applications in automatic control.

Bayesian Inference for Zero-Modified Power Series Regression Models

Count data often exhibit discrepancies in the frequencies of zeros, which commonly occur across various application domains. These data may include excess zeros (zero inflation) or, less frequently, a scarcity of zeros (zero deflation). In regression models, both situations can arise at different levels of covariates. The zero-modified power series regression model provides an effective framework for modeling such count data, as it does not require prior knowledge of the type of zero modification, whether zero inflation or zero deflation, and can accommodate overdispersion, equidispersion, or underdispersion present in the data. This paper proposes a Bayesian estimation procedure based on the stochastic gradient Hamiltonian Monte Carlo algorithm, effectively addressing many challenges associated with estimating the model parameters. Additionally, we introduce a measure of Bayesian efficiency to evaluate the impact of prior information on parameter estimation. The practical utility of the proposed method is demonstrated through both simulated and real data across different types of zero modification.

Parameter Estimate and Adaptive Control of DARMA Systems With Uniform Quantized Output Data

ABSTRACTThis paper is concerned with parameter estimate and adaptive control problems of deterministic autoregressive moving average (DARMA) systems on the basis of quantized data of system output signals which are generated by a kind of uniform quantizer. By designing system input signals, the extended least‐squares (ELS) algorithm with uniform output observations is proved to yield bounded estimation errors under some mild assumptions. Moreover, the adaptive tracking controller under inaccuracy observations is also designed. To analyse the properties of tracking error, we use the expanded form of the ELS and research the boundedness of quantization noise. In addition, we give the expression of tracking error and show how it depends on the size of quantization step when the quantization step satisfies some conditions. A numerical example is supplied to demonstrate the theoretical results.

Estimation of Genetic Parameters and GWAS on Water Efficiency Traits in the Senepol Cattle.

The need for producing in environmentally resilient system drives new research to achieve sustainable beef production. Water footprint of the beef supply chain is a concern that must be addressed, aiming to improve water use within the production chain. One approach is genetic selection of beef cattle for water efficiency. However, it is essential to understand the genetic architecture and mechanisms involved in the expression of this phenotype to choose the best selection criteria. Thus, our study aimed to estimate genetic parameters for water efficiency traits, conduct a genome-wide association study (GWAS) and identify the genetic networks and biological processes involved. A population of 1762 purebred Senepol cattle was phenotyped for the following water efficiency traits: water intake (WI), gross water efficiency (GWE), water conversion ratio (WCR), residual water intake based on average daily gain (RWIADG) and residual water intake based on dry matter intake (RWIDMI). A subset of 1342 animals was genotyped using GGP Bovine 50 K SNP Chip with (734 animals) or 100 K (508 animals), and imputation from 50 K to 100 K was performed with Beagle software. The heritability estimates were 0.36 ± 0.06, 0.26 ± 0.05, 0.22 ± 0.05, 0.24 ± 0.05 and 0.20 ± 0.05 for WI, GWE, WCR, RWIADG and RWIDMI, respectively. Unlike the raw measures of WI, the phenotypic correlations between average daily gain (ADG) and the residuals (RWIDMI and RWIADG) were zero. All water efficiency traits were moderately to highly correlated with each other. GWAS were used to estimate the effect of 79,860 single nucleotide polymorphisms (SNPs), and significant SNPs were only observed for WCR. Enrichment analysis of genes in the significant regions revealed the involvement of different biological processes, such as saliva production, water transport, renal system and immune system. Genetic selection of Senepol cattle for water efficiency traits is feasible and can reduce water requirements for meat production. Water efficiency measures are polygenic traits, and different biological processes act simultaneously on the expression of related phenotypes.

Estimating the Budget Impact and Joint Replacement Avoidance by Implementing a Standardized Education and Exercise Therapy Program for Hip and Knee Osteoarthritis in a Publicly Insured Healthcare System.

Estimate the budget impact of funding a standardized education and exercise therapy program (GLA:D®) for people with hip and knee OA waiting for total joint replacement (TJR) consultation in a universal publicly insured healthcare system in Canada. We built a budget impact analysis (BIA) model to estimate the annual cost of providing GLA:D® program to people waiting for TJR consultation and then forecasted a three-year budget cycle. The base case assumes 40% attend GLA:D®, 11% avoid surgery, uniform care delivery, training costs are incurred separately, and the healthcare system has enough trained staff to meet demand. The population of people with hip and knee OA waiting for TJR consultation was estimated with government statistics, peer-reviewed evidence and routinely collected data from five orthopaedic centralized intake clinics (serving 80% of people seeking TJR). Patient-level costs were collected prospectively. International published evidence-informed estimates of TJR avoidance. One-way sensitivity analysis of key parameters evaluated model robustness. Four scenarios were analysed: public-funding for everyone (base case), low-income, rural, or uninsured persons. Funding GLA:D® would cost $4.3 million, serve 12,500 people, and save $8.5 million by avoiding 1,300 TJRs in year one. Savings grow to $8.8 and $8.7 million in year two and three. The number of TJR's performed annually produced the most uncertainty in budget impact (-$15.3, -$1.8 million). The most cautious parameter estimates still produce cost-savings. Publicly funding standardized education and exercise therapy programs for everyone waiting for TJR consultation would avoid surgeries, improve access to evidence-based treatments and save more than the program costs.

MLody—Deep Learning–generated Polarized Synchrotron Coefficients

Abstract Polarized synchrotron emission is a fundamental process in high-energy astrophysics, particularly in the environments around black holes and pulsars. Accurate modeling of this emission requires precise computation of the emission, absorption, rotation, and conversion coefficients, which are critical for radiative transfer simulations. Traditionally, these coefficients are derived using fit functions based on precomputed ground truth values. However, these fit functions often lack accuracy, particularly in specific plasma conditions not well represented in the data sets used to generate them. In this work, we introduce MLody, a deep neural network designed to compute polarized synchrotron coefficients with high accuracy across a wide range of plasma parameters. We demonstrate MLody's capabilities by integrating it with a radiative transfer code to generate synthetic polarized synchrotron images for an accreting black hole simulation. Our results reveal significant differences, up to a factor of 2, in both linear and circular polarization compared to traditional methods. These differences could have important implications for parameter estimation in Event Horizon Telescope observations, suggesting that MLody could enhance the accuracy of future astrophysical analyses.

Quantitative analysis model of wind energy resources in adjacent area of existing wind farms

To accurately analyze the risk resource reserves in the vicinity of wind farms and optimize the application of wind energy resources, a quantitative analysis model of wind energy resources in the vicinity of existing wind farms considering the roughness index and interference characteristics is studied. Collect and preprocess the wind energy resource data in the vicinity of existing wind farms in the national meteorological-related Internet, and build a risk resource analysis index system consisting of wind energy density, roughness index and interference characteristics according to relevant national standards; The parameter estimation method of Weibull distribution is used to calculate the density index of wind energy resources and the available hours index of wind energy resources. The roughness index is determined using the Monin–Obukhov consistent theory, and the correlation coefficient and stability are used to describe the interference characteristic index. The quantitative analysis results of wind energy resources in the vicinity of existing wind farms are obtained according to the calculation results. The experimental results show that the model can accurately calculate the roughness index and interference characteristics of the study area, and obtain high-precision quantitative analysis results for wind energy resources.

TRADUÇÃO E VALIDAÇÃO DE UM QUESTIONÁRIO DE DUAS CAMADAS SOBRE CIRCUITOS ELÉTRICOS SIMPLES

Research on teaching electric circuits has highlighted students´ difficulties in the construction of a solid and scientifically consistent mental model. A two-tier simple electric circuits test (2T-SEC Test), designed to assess knowledge about simple electric circuits, was translated and administered to 223 students in the 9th and 10th grades of a Portuguese school, with the aim of validating a Portuguese language version of the questionnaire. An Item Response Theory analysis (two-parameter logistic model) was used in the validation. The internal consistency of the test for the 9th and 10th grades (Cronbach's alpha of 0,88 and 0,86, respectively) and the estimates of the item parameters (discrimination and difficulty) show that the test is reliable, valid, and suitable as a post-test for middle/high school Portuguese students. The results indicate that the questionnaire has greater information for students with an intermediate level of performance and confirm the existing literature: the concept of electrical voltage is delicate and complex, and most students take time to develop a solid and consistent mental model of electric circuits.

A methodology based on the fuzzy identification for a hybrid method in times series forecasting models

This work presents a hybrid forecasting method, with concept and improvement through the fuzzy set’s theory coupled with the classic time series method. This study also proposed an identification tool that has not been published in the scientific literature for parametric point identification and estimation within the classical time series methodology combined with fuzzy sets theory, which generated a hybrid identification technique in fuzzy time data. In comparison to the forecasting methods consolidated in the literature, the results presented by the proposed method have been considered fully satisfactory due to the minimal error observed.

Cautious parameter learning for a joint mean and variance monitoring CUSUM scheme with guaranteed in-control performance

Joint monitoring of the mean and variance of a normal process is crucial in quality engineering for assessing homoscedasticity, determining process capability, and recognizing that changes in scale often accompany changes in location. A CUSUM scheme for monitoring both mean and variance must ensure overall performance, even with estimated parameters. Approaches guaranteeing minimum in-control performance are standard, but they lose power with small Phase I samples. Learning approaches allow parameter re-estimation with new data, while cautious schemes define update rules to minimize sample contamination from out-of-control observations. This article presents a CUSUM scheme incorporating cautious parameter learning and guaranteed in-control performance for joint monitoring of mean and variance with normal, independent observations. Our novel approach compares parameter estimation procedures based on conditional expected delay and false alarm probability across a series of change points for a profile assessment, rather than a point-based assessment. Computational results show that the cautious learning procedure using a conservative external updating rule is preferred even when the initial sample size is small, as it provides the best balance between conditional expected delay and false alarm probability from a practical perspective.

A Novel Filtering Based Maximum Likelihood Generalized Extended Gradient Method for Multivariable Nonlinear Systems

ABSTRACTThis study proposes a filtering based maximum likelihood generalized extended gradient algorithm for multivariable nonlinear systems with autoregressive moving average noises. The parameter estimates are obtained by minimizing the half squared residual measurement which can approach the true values. An auxiliary model is also established with the measurable information of the system, and the output of the auxiliary model is used to replace the unmeasurable variables of the system, so that the output of the auxiliary model approximates these unmeasurable variables, so as to obtain the consistent estimation of the system parameters. A maximum likelihood generalized extended gradient algorithm is derived for comparison and a numerical example is provided to show the effectiveness of the proposed method and the estimates converge to the actual values quickly.

Predictors of Left Ventricular Ejection Fraction Decrease in Patients With ST-Segment Elevation Myocardial Infarction.

To identify predictors and construct a model for predicting left ventricular (LV) ejection fraction (EF) in patients with ST-segment elevation myocardial infarction (STEMI). This was a prospective registry study of patients with STEMI admitted within the first 24 hours of the disease onset. Patients were evaluated and treated according to the current clinical guidelines. On the first day of STEMI, concentrations of growth stimulating factor, proprotein convertase subtilisin-kexin type 9 (PCSK9), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and high-sensitivity troponin I and C-reactive protein were measured. Echocardiography was performed on the first day and on day 10-12 of admission; LVEF was calculated by the Simpson method. The study included 138 patients; 3 patients were excluded from this part of the study due to death before repeat echocardiography. Based on the LVEF value on day 10-12 of STEMI, the patients were divided into the groups with preserved LVEF (pLVEF) ≥50% (n=34), reduced LVEF (rLVEF) ≤40% (n=21), and moderately reduced LVEF (mrLVEF) 41-49% (n=80). The ordinal regression analysis showed that the factors influencing LVEF in STEMI patients included a history of chronic heart failure, Killip class II-IV acute heart failure at the index hospitalization, the development of LV dilation and postinfarction aneurysm, and an increase in NTproBNP. Based on the obtained estimates of the regression parameters, a prognostic model was constructed that showed the highest sensitivity of the model for predicting rLVEF, 94.4%, mrLVEF, 92.9%, and a lower sensitivity for predicting pLVEF, 62.5%. In the presence of a history of chronic heart failure, Killip class II-IV acute heart failure, developed LV dilation and postinfarction aneurism, and elevated NTproBNP, patients with STEMI are expected to have lower LVEF values.

Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis

The article presents the development of a Rules-Based Energy Management System for a nanogrid that serves an electric vehicle charging station. This nanogrid is composed of photovoltaic generation, a wind turbine, a battery energy storage system, and a fast electric vehicle charger. The objective is to prioritize the use of renewable energy sources, reducing costs and promoting energy efficiency. The methodology includes forecasting models based on an Artificial Neural Network for photovoltaic generation, a parametric estimation for wind generation, and a Monte Carlo simulation to predict the energy consumption of electric vehicles. The developed algorithm makes decisions every 15 min, considering variables such as energy tariff, battery state of charge, renewable generation forecast, and energy consumption forecast. The results showed that the system adequately balances energy generation, consumption, and storage, even under forecasting uncertainties. The use of the Monte Carlo simulation was crucial for evaluating the financial impacts of forecast errors, enabling robust decision-making. This energy management system proved to be effective and sustainable for nanogrids dedicated to electric vehicle charging, with the potential to reduce operational costs and increase energy reliability and the use of renewable energy sources.

A sparse latent class model incorporating response times.

Diagnostic models (DM) have been widely used to classify respondents' latent attributes in cognitive and non-cognitive assessments. The integration of response times (RTs) with DM presents additional evidence to understand respondents' problem-solving behaviours. While recent research has explored using sparse latent class models (SLCM) to infer the latent structure of items based on item responses, the incorporation of RT data within these models remains underexplored. This study extends the SLCM framework to include RT, relaxing the conditional independence assumption between RT and latent attributes given individual speed. This adaptation provides a more flexible framework for jointly modelling RT and item responses. While the proposed model holds promise for applications in educational assessment, this study applied the model to the Fisher Temperament Inventory, yielding findings that provide a novel perspective on utilizing DM with RT in personality assessments. Additionally, a Gibbs sampling algorithm is proposed for parameter estimation. Results from Monte Carlo simulations demonstrate the algorithm's accuracy and efficiency.