Two-step Estimation Method Research Articles

Enhancing Effort-Moderated Item Response Theory Models by Evaluating a Two-Step Estimation Method and Multidimensional Variations on the Model.

Rapid-guessing behavior in data can compromise our ability to estimate item and person parameters accurately. Consequently, it is crucial to model data with rapid-guessing patterns in a way that can produce unbiased ability estimates. This study proposes and evaluates three alternative modeling approaches that follow the logic of the effort-moderated item response theory model (EM-IRT) to analyze response data with rapid-guessing responses. One is the two-step EM-IRT model, which utilizes the item parameters estimated by respondents without rapid-guessing behavior and was initially proposed by Rios and Soland without further investigation. The other two models are effort-moderated multidimensional models (EM-MIRT), which we introduce in this study and vary as both between-item and within-item structures. The advantage of the EM-MIRT model is to account for the underlying relationship between rapid-guessing propensity and ability. The three models were compared with the traditional EM-IRT model regarding the accuracy of parameter recovery in various simulated conditions. Results demonstrated that the two-step EM-IRT and between-item EM-MIRT model consistently outperformed the traditional EM-IRT model under various conditions, with the two-step EM-IRT estimation generally delivering the best performance, especially for ability and item difficulty parameters estimation. In addition, different rapid-guessing patterns (i.e., difficulty-based, changing state, and decreasing effort) did not affect the performance of the two-step EM-IRT model. Overall, the findings suggest that the EM-IRT model with the two-step parameter estimation method can be applied in practice for estimating ability in the presence of rapid-guessing responses due to its accuracy and efficiency. The between-item EM-MIRT model can be used as an alternative model when there is no significant mean difference in the ability estimates between examinees who exhibit rapid-guessing behavior and those who do not.

문화예술부문에 대한 재정지출이 온실가스배출에 미치는 영향분석

As the culture and arts sector develops alongside economic growth, the increase in greenhouse gas emissions highlights the importance of environmental considerations in the context of climate change. This study employs a two-step estimation method using a dynamic panel model to analyze the impact of fiscal expenditure for the culture and arts sector on greenhouse gas emissions, distinguishing between direct and indirect effects. The first-stage analysis reveals that increased fiscal expenditure in the culture and arts sector positively impacts regional economic growth. The second-stage analysis indicates the presence of an inverted U-shaped curve between per-capita GRDP and greenhouse gas emissions, supporting the Environmental Kuznets Curve hypothesis. While fiscal expenditure for the culture and arts sector directly increases greenhouse gas emissions, it indirectly reduces them through regional economic growth. However, the direct effect is greater and more significant than the indirect effect, resulting in a net increase in greenhouse gas emissions. These findings suggest that for effective greenhouse gas reduction policies at the local government level, it is necessary to improve energy efficiency in cultural and artistic activities and promote the use of eco-friendly energy sources. Additionally, there is a need for support that can raise environmental awareness among cultural and artistic professionals and induce changes in their energy consumption patterns.

A novel skewed generalized normal distribution: properties, statistical inference, and its applications

In this paper, a novel skewed generalized normal distribution (NSGN), which encompasses several well-known distributions, is a two-component mixture model based on the generalized normal distribution. The NSGN distribution offers flexibility in modeling skewed data characterized by high kurtosis and strong skewness, making it highly applicable in finance, medicine, and engineering. The primary characteristics and properties of this new distribution are introduced, and the explicit expressions for the moments of order statistics are provided using recursive relations. The maximum likelihood estimation based on a profile likelihood approach, L-moments estimation, and a two-step estimation method combining Bayesian posterior maximum estimation with moment estimates are presented to estimate the four parameters of NSGN distribution. A simulation study compares the performance of these methods across different sample sizes and different parameters. The appropriateness of the proposed distribution has been tested by comparing it with several skewed distributions. Additionally, applications to real datasets showcase the beneficial properties of the NSGN for applied statistical research.

Approximate parameter estimation and mis-specification analysis of degradation model with asymmetric random effects

Asymmetric distributions such as the skew-normal (SN) distribution have been used for modeling the unit-to-unit variation of the degradation rate of products, and its superiority for model fitting and lifetime estimation has been demonstrated in literature. However, there still exist two issues that need to be further studied. That is, how to simply estimate the parameters of the degradation model with asymmetric random effects and whether the asymmetry or skewness affects the remaining useful life (RUL) prediction. Driven by these two issues, the parameter estimation and RUL prediction of the degradation model with general random effects are first introduced and a two-step maximum likelihood estimation (MLE) method for parameter estimation is proposed, which could greatly simplify the parameter estimation of the degradation model with asymmetric random effects. Then, the SN distribution is used as an example to analyze the second issue regarding the effect of model mis-specification on RUL prediction based on regression models and Wiener processes with different asymmetric random effects in theory. Finally, the parameter estimation and mis-specification analysis of degradation models with other asymmetric random effects are conducted through numerical examples and two case studies. The results demonstrate that the proposed two-step MLE method is satisfactory and the effect of asymmetry on RUL prediction is negligible. Therefore, there is no need to consider the asymmetry of random effects for RUL prediction.

Oracle-efficient estimation and trend inference in non-stationary time series with trend and heteroscedastic ARMA error

The non-stationary time series often contain an unknown trend and unobserved error terms. The error terms in the proposed model consist of a smooth variance function and the latent stationary ARMA series, which allows heteroscedasticity at different time points. The theoretically justified two-step B-spline estimation method is proposed for the trend and variance function in the model, and then residuals are obtained by removing the trend and variance function estimators from the data. The maximum likelihood estimator (MLE) for the latent ARMA error coefficients based on the residuals is shown to be oracally efficient in the sense that it has the same asymptotic distribution as the infeasible MLE if the trend and variance function were known. In addition to the oracle efficiency, a kernel estimator is obtained for the trend function and shown to converge to the Gumbel distribution. It yields an asymptotically correct simultaneous confidence band (SCB) for the trend function, which can be used to test the specific form of trend. A simulation-based procedure is proposed to implement the SCB, and simulation and real data analysis illustrate the finite sample performance.

A Two-Step Estimation Method for a Time-Varying INAR Model

This paper proposes a new time-varying integer-valued autoregressive (TV-INAR) model with a state vector following a logistic regression structure. Since the autoregressive coefficient in the model is time-dependent, the Kalman-smoothed method is applicable. Some statistical properties of the model are established. To estimate the parameters of the model, a two-step estimation method is proposed. In the first step, the Kalman-smoothed estimation method, which is suitable for handling time-dependent systems and nonstationary stochastic processes, is utilized to estimate the time-varying parameters. In the second step, conditional least squares is used to estimate the parameter in the error term. This proposed method allows estimating the parameters in the nonlinear model and deriving the analytical solutions. The performance of the estimation method is evaluated through simulation studies. The model is then validated using actual time series data.

Does debt moderate the impact of family commissioner boards on company performance in Indonesia?

This study aims to investigate the influence of family commissioner boards (FCBs) on the operational efficiency of companies in Indonesia that use debt as a control tool, which includes bank and non-bank debt. Using the two-step GMM-First Difference estimation method, the research sample consists of 121 family-owned companies using unbalanced panel data from 2009 to 2018. This investigation produces several significant findings. Firstly, the results of the analysis show that the presence of family representatives on the board of commissioners has a negative impact on overall company performance. These observations suggest that FCBs may prioritize the interests of family shareholders over minority shareholders, which indicates entrenchment behavior. Second, the analytical results reveal that debt plays a moderating role in the influence of FCB on company performance. Debt acts as a deterrent to entrenchment behavior, thereby improving firm performance. Third, the results of the analysis did not find significant differences in FCB entrenchment behavior between companies that have bank debt and companies that have non-bank debt. These findings have significant policy implications for regulatory bodies in Indonesia regarding the governance of family-owned companies. It is vital to establish a mechanism for appointing family members to the board of commissioners that protects the interests of all shareholders and promotes a fairer corporate landscape.

Accelerated degradation data analysis based on inverse Gaussian process with unit heterogeneity

The unit heterogeneity of products and the nonlinear parameter-stress relationship often exist in practice. Therefore, considering the unit heterogeneity, the nonlinear accelerated model and inverse Gaussian process are developed to depict the accelerated degradation data. On the other hand, this more realistic model leads a challenge to derive the model parameter interval estimation. Thereby, a novel two-step interval estimation method is proposed for the proposed accelerated degradation model. First, generalized confidence intervals of the parameters characterizing random effect are derived from the Cornish–Fisher expansion, and their cumulative distribution functions are obtained. Then, using the generalized pivotal quantity procedure, generalized confidence intervals of the accelerated model parameters are derived. In addition, generalized confidence intervals of predictive reliability indexes are derived to guide the practical application. Finally, simulation studies and two real examples on Spiral springs and integrated circuit devices are presented to demonstrate the implementation of the proposed method.

Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold

Remaining useful life (RUL) prediction is one of the most crucial components in prognostics and health management (PHM) of aero-engines. This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold. Firstly, a random-coefficient regression (RCR) model is used to model the degradation process of aeroengines. Then, the RUL distribution based on fixed failure threshold is derived. The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation (MLE) method and the random coefficient is updated in real time under the Bayesian framework. The failure threshold in this paper is defined by the actual degradation process of aeroengines. After that, a expectation maximization (EM) algorithm is proposed to estimate the underlying failure threshold of aeroengines. In addition, the conditional probability is used to satisfy the limitation of failure threshold. Then, based on above results, an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold (RFT) is derived in a closed-form. Finally, a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.

Semi-supervised Learning Method for Source Range Estimation in Shallow Water

Data-driven machine learning methods in recent years have shown great vitality in underwater acoustic, especially in the localization of underwater sound sources. Meanwhile, there exists a great difficulty in the acquisition and processing of underwater acoustic data, which caused the lack of label data. This paper proposes a two-step semi-supervised learning-based range estimation method for shallow ocean sound sources, which utilizes semi-supervised learning in deep learning to alleviate the problem of limited access to underwater acoustic label data and relatively abundant unlabelled data in practical scenarios. The performance of the method has been validated on SWellEx-96 experiment data.

On Band Stitching for Wideband Vector Measurements With Vector Signal Analyzers

As the bandwidth of radio signals increases significantly over the evolution of communication systems, the analysis bandwidth of many current instruments becomes insufficient. In this article, we study analysis bandwidth extension using the so-called band stitching. Specifically, we propose a two-step delay estimation method, which combines the cross correlation and an additional refining step to resolve the timing impairment between different sampling branches in postprocessing. We verify the method with a sequential measurement setup, using vector signal analyzers (VSAs). The result shows the necessity of conducting the refining step for getting a more accurate delay estimate to successfully recover the original wideband signal. Besides, a good agreement is found between the stitched data and the reference data in terms of the waveforms and error vector magnitude (EVM), which shows the effectiveness and robustness of the proposed calibration method in practice.

Productivity determinants in the service industry: differences between high and low knowledge intensive sectors

PropósitoLa industria de servicios se ha convertido en un sector importante para el crecimiento económico, particularmente en los países en desarrollo. En este contexto, el objetivo de este artículo es comparar los determinantes de la productividad entre empresas que operan en sectores de servicios de baja y alta intensidad de conocimiento (LKIS y HKIS) en Ecuador.Diseño/metodología/enfoqueUtilizamos un método de estimación de dos pasos. En primer lugar, estimamos la productividad de las empresas y, en segundo lugar, los determinantes de la productividad. Para esto, utilizamos una base de datos de panel no balanceado sobre los estados financieros de las empresas formales ecuatorianas de los años 2007 a 2018.HallazgosNuestros resultados muestran que las empresas LKIS son ligeramente más intensivas en mano de obra en comparación con las empresas HKIS. Los determinantes de la productividad son similares entre las empresas HKIS y LKIS, excepto por las exportaciones y la concentración del mercado. Las empresas HKIS son más productivas cuando el nivel de competencia es bajo, lo que indica que un mayor poder de mercado está asociado con una mayor productividad. La influencia de los impuestos sobre la productividad depende del tamaño de la empresa. Las pequeñas y medianas empresas se ven más negativamente afectadas que las grandes empresas.Implicaciones prácticasLos impuestos deben diseñarse considerando el tamaño de las empresas, ya que podrían afectar su productividad. Por lo tanto, impuestos más bajos para las pequeñas y medianas empresas pueden reducir la desigualdad entre el tamaño de las empresas. Además, el conocimiento que tienen las HKIS debe extenderse a otras empresas convirtiéndose en una externalidad positiva en lugar de una barrera de entrada.Originalidad/valorA pesar de que los determinantes de la productividad del sector servicios se han explorado recientemente, en contraste con el sector manufacturero, los determinantes individuales y contextuales están menos identificados. En este artículo utilizamos un amplio conjunto de características de las empresas que podrían afectar la productividad en las empresas de servicios.

Optimal Micro-PMU Placement for Improving State Estimation Accuracy via Mixed-integer Semidefinite Programming

Micro-phasor measurement units (µPMUs) with a micro-second resolution and milli-degree accuracy capability are expected to play an important role in improving the state estimation accuracy in the distribution network with increasing penetration of distributed generations. Therefore, this paper investigates the problem of how to place a limited number of µPMUs to improve the state estimation accuracy. Combined with pseudo-measurements and supervisory control and data acquisition (SCADA) measurements, an optimal µPMU placement model is proposed based on a two-step state estimation method. The E-optimal experimental criterion is utilized to measure the state estimation accuracy. The nonlinear optimization problem is transformed into a mixed-integer semidefinite programming (MISDP) problem, whose optimal solution can be obtained by using the improved Benders decomposition method. Simulations on several systems are carried out to evaluate the effective performance of the proposed model.

Two-Step Unbiased Estimation on Small-Delay Multipath of GNSS Signals Based on S-Curve Bias

Multipath error is among the main error sources of global navigation satellite systems (GNSSs). The traditional estimation method represented by the multipath estimating delay-locked loop (MEDLL) has difficulty in distinguishing small-delay multipath signals and noise during iterations, which leads to a bias in the estimation of small-delay multipath signals. To mitigate this small-delay multipath estimation bias, a two-step unbiased estimation method based on the S-curve bias (SCB) is proposed. First, the relationship among SCB, multipath parameters, and correlation spacing is derived. Second, a search approach based on the least squares is designed to obtain the turning point position of the SCB results with different correlation spacings. Third, the multipath delay can be estimated through the turning point of the SCB results. Finally, the phase and amplitude ratio of the direct signal and multipath signals is estimated by the SCB results of local carrier signal replicas with different phases. The proposed method does not contain iteration steps, which minimizes the accuracy influence introduced by the iterations. Simulations using the Spirent simulator and real signal experiment results show that the proposed algorithm can reduce the small-delay multipath error effectively with low computational complexity. Compared with MEDLL, Teager and Kaiser-MEDLL (TK-MEDLL), and high-resolution correlator (HRC) algorithms, the small-delay multipath mitigation performance of the proposed algorithm was improved by approximately 82.9%, 74.5%, and 91.7%, respectively. Therefore, the proposed algorithm can mitigate estimation bias in small-delay multipath signals effectively. This method provides a new strategy for the parametric estimation of the multipath signals of GNSS.

Machine learning-assisted ensemble analysis for the prediction of urinary tract infection in elderly patients with ovarian cancer after cytoreductive surgery

Urinary tract infection (UTI) is a common type of postoperative infection following cytoreductive surgery for ovarian cancer, which severely impacts the prognosis and quality of life of patients. To develop a machine learning assistant model for the prevention and control of nosocomial infection. A total of 674 elderly patients with ovarian cancer who were treated at the Department of Gynaecology at Jingzhou Central Hospital between January 31, 2016 and January 31, 2022 and met the inclusion criteria of the study were selected as the research subjects. A retrospective analysis of the postoperative UTI and related factors was performed by reviewing the medical records. Five machine learning-assisted models were developed using two-step estimation methods from the candidate predictive variables. The robustness and clinical applicability of each model were assessed using the receiver operating characteristic curve, decision curve analysis and clinical impact curve. A total of 12 candidate variables were eventually included in the UTI prediction model. Models constructed using the random forest classifier, support vector machine, extreme gradient boosting, and artificial neural network and decision tree had areas under the receiver operating characteristic curve ranging from 0.776 to 0.925. The random forest classifier model, which incorporated factors such as age, body mass index, catheter, catheter intubation times, blood loss, diabetes and hypoproteinaemia, had the highest predictive accuracy. These findings demonstrate that the machine learning-based prediction model developed using the random forest classifier can be used to identify elderly patients with ovarian cancer who may have postoperative UTI. This can help with treatment decisions and enhance clinical outcomes.

Reliability modelling considering multi-dimensional cognitive uncertainties based on uncertainty theory

Aiming at the multi-dimensional cognitive uncertainties in performance degradation modelling for small samples, a reliability assessment method is proposed based on uncertainty theory. First, a degradation model is established based on the uncertain process, which considers cognitive uncertainties from the time fluctuation, the measurement error, and the individual differences in initial value and degradation rate, and the reliability function is derived. Then, a two-step parameter estimation method for the degradation model is proposed based on the α-path and the least square, which is verified by numerical simulation. Finally, the effectiveness and superiority of the proposed method are verified by the cases of GaAs lasers and magneto-optic data storage disks, and parameter sensitivity analysis is conducted. The results indicate that uncertainty affects product reliability greatly. For small samples, thedegradation models considering multi-dimensional uncertainties based on the Liu process has smaller fitting errors than that based on the Wiener process.

An Uncertainty Weighted Non-Cooperative Target Pose Estimation Algorithm, Based on Intersecting Vectors

Aiming at the relative pose estimation of non-cooperative targets in space traffic management tasks, a two-step pose estimation method, based on spatially intersecting straight lines, is proposed, which mainly includes three aspects: (1) Use binocular vision to reconstruct the straight space line, and based on the direction vector of the straight line and the intersection of the straight line, solve the pose of the measured target in the measurement coordinate system, and obtain the initial value of the pose estimation. (2) Analyze the uncertainty of the spatial straight-line imaging, construct the uncertainty description matrix of the line, and filter the line features, accordingly. (3) Analyze the problems existing in the current linear distance measurement, construct the spatial linear back-projection error in the parametric coordinate space, and use the linear imaging uncertainty to weigh the projection error term to establish the optimization objective function of the pose estimation. Finally, the nonlinear optimization algorithm is used to iteratively solve the above optimization problem, to obtain high-precision pose estimation results. The experimental results show that the two-step pose estimation algorithm, proposed in this paper, can effectively achieve a high-precision and robust pose estimation for non-cooperative spatial targets. When the measurement distance is 10 m, the position accuracy can reach 10 mm, and the attitude measurement accuracy can reach 1°, which meets the pose estimation accuracy requirements of space traffic management.

Development and Validation of Machine Learning Models for Prediction of Fracture Risk in Patients with Elderly-Onset Rheumatoid Arthritis.

ObjectiveFracture is a critical unfavorable prognostic factor in patients with rheumatoid arthritis(RA) and osteoporosis. At present, models involving clinical indices that accurately predict fracture are still uncommon. We addressed this gap by developing machine learning (ML)-based predictive models to individualize the risk of fracture in elderly patients with RA and osteoporosis and to identify a high-risk group for fracture.Methods487 patients diagnosed with RA and osteoporosis at the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture were randomly divided into a training cohort (used for building the model) and a validation cohort (used for validating the model). Five ML-assisted models were developed from candidate clinical features using two-step estimation methods. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model.ResultsA total of twenty-two candidate variables were included, and the prediction model was established by an ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, artificial neural network (ANN), support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), probability of major osteoporotic fractures (PMOF), and probability of hip fracture (PHF) ranged from 0.695 to 0.878. Among them, RFC obtained the optimal prediction efficiency via adding serum selenium and clinical indices, that is, glucocorticoid, and erythrocyte sedimentation rate (ESR).ConclusionBased on the classic clinical parameters, the fracture risk of RA patients with osteoporosis can be accurately predicted. In particular, RFC prediction model shows good discrimination ability in identifying high-risk patients with fracture.

The Bivariate Unit-Sinh-Normal Distribution and Its Related Regression Model

In this paper, a new bivariate absolutely continuous probability distribution is introduced. The new distribution, which is called the bivariate unit-sinh-normal (BVUSHN) distribution, arises by applying a transformation to the bivariate Birnbaum–Saunders distribution (BVBS). The main properties of the new proposal are studied in detail. In addition, from the new distribution, the BVUSHN regression model is also introduced. For both the bivariate probability distribution and the respective associated regression model, parameter estimation is conducted from a classical approach by using the maximum likelihood method together with the two-step estimation method. A small Monte Carlo simulation study is carried out to evaluate the behavior of the used estimation method and the properties of the estimators. Finally, for illustrative purposes, two applications with real data are presented in which the usefulness of the proposals is evidenced.

A function-based approach to model the measurement error in wearable devices.

Physical activity (PA) is an important risk factor for many health outcomes. Wearable-devices such as accelerometers are increasingly used in biomedical studies to understand the associations between PA and health outcomes. Statistical analyses involving accelerometer data are challenging due to the following three characteristics: (i) high-dimensionality, (ii) temporal dependence, and (iii) measurement error. To address these challenges we treat accelerometer-based measures of PA as a single function-valued covariate prone to measurement error. Specifically, in order to determine the relationship between PA and a health outcome of interest, we propose a regression model with a functional covariate that accounts for measurement error. Using regression calibration, we develop a two-step estimation method for the model parameters and establish their consistency. A test is also proposed to test the significance of the estimated model parameters. Simulation studies are conducted to compare the proposed methods with existing alternative approaches under varying scenarios. Finally, the developed methods are used to assess the relationship between PA intensity and BMI obtained from the National Health and Nutrition Examination Survey data.