Robust Estimation Results Research Articles

Revisiting the nexus between development aid, institutions, and growth: A global evidence

AbstractObjectiveThis study mainly focuses on the relationship between development aid and growth as well as examines the role of the institutional development levels of countries in this relationship and makes economic inferences.MethodsThe analysis is conducted for 64 countries receiving official development assistance in the 2000–2020 period. To provide robust estimates in these studies, the Driscoll and Kraay estimator is used after fixed and random effects models.ResultsIn almost all of the estimated models, a significant relationship between official development assistance per capita and growth is found. Moreover, the models with institutional factors and interaction terms also support the results. According to the robust estimation results, official development assistance per capita positively affects growth.ConclusionThe results for all countries do not mean that good policies and strong institutions are unnecessary. In contrast, they show that the priority is the sum of aid per capita.

Bayesian relative composite quantile regression approach of ordinal latent regression model with L1/2 regularization

AbstractOrdinal data frequently occur in various fields such as knowledge level assessment, credit rating, clinical disease diagnosis, and psychological evaluation. The classic models including cumulative logistic regression or probit regression are often used to model such ordinal data. But these modeling approaches conditionally depict the mean characteristic of response variable on a cluster of predictive variables, which often results in non‐robust estimation results. As a considerable alternative, composite quantile regression (CQR) approach is usually employed to gain more robust and relatively efficient results. In this paper, we propose a Bayesian CQR modeling approach for ordinal latent regression model. In order to overcome the recognizability problem of the considered model and obtain more robust estimation results, we advocate to using the Bayesian relative CQR approach to estimate regression parameters. Additionally, in regression modeling, it is a highly desirable task to obtain a parsimonious model that retains only important covariates. We incorporate the Bayesian penalty into the ordinal latent CQR regression model to simultaneously conduct parameter estimation and variable selection. Finally, the proposed Bayesian relative CQR approach is illustrated by Monte Carlo simulations and a real data application. Simulation results and real data examples show that the suggested Bayesian relative CQR approach has good performance for the ordinal regression models.

Mechanisms for realizing the ecological products value: Green finance intervention and support

The essence of ecological product value is the rational development and utilization of natural ecological and environmental resources, which are converted into social attributes or economic value. An accurate assessment of the value of ecological products is a prerequisite for realizing their economic value. However, few studies have measured the interaction mechanism between green finance and ecological products value. This study measured the ecological products value at the city level in China from 2010 to 2021, based on the dynamic equivalent moderator model. A spatial panel data model was used to analyze the spatial pattern and dynamic evolution of ecological products value and identify their driving factors. The ecological products value in China showed an increasing trend from 2010 to 2021, and there were obvious spatial correlations between regions. Green finance significantly contributes to the realization of ecological products value, and robust estimation results can still be obtained after controlling for endogeneity. Meanwhile, the role of green finance has a spatial spillover effect. This study provides a new research perspective for correctly assessing ecological products value and studying the role of green finance in realizing ecological products value.

Probabilistic model updating of steel frame structures using strain and acceleration measurements: A multitask learning framework

This paper proposes a multitask learning framework for probabilistic model updating by jointly using strain and acceleration measurements. This framework can enhance the structural damage assessment and response prediction of existing steel frame structures with quantified uncertainty. Multitask learning may be used to address multiple similar inference tasks simultaneously to achieve a more robust prediction performance by transferring useful knowledge from one task to another, even in situations of data scarcity. In the proposed model-updating procedure, a spatial frame is decomposed into multiple planar frames that are viewed as multiple tasks and jointly analyzed based on the hierarchical Bayesian model, leading to robust estimation results. The procedure uses a displacement–stress relationship in the modal space because it directly reflects the elemental stiffness and requires no prior knowledge concerning the mass, unlike most existing model-updating techniques. Validation of the proposed framework by using a full-scale vibration test on a one-story, one-bay by one-bay moment resisting steel frame, wherein structural damage to the column bases is simulated by loosening the anchor bolts, is presented. The experimental results suggest that the displacement–stress relationship has sufficient sensitivity toward localized damage, and the Bayesian multitask learning approach may result in the efficient use of measurements such that the uncertainty involved in model parameter estimation is reduced. The proposed framework facilitates more robust and informative model updating.

Infrastructure and Manufacturing Sector Performance in Nigeria

The objective of this study was to investigate the effect of infrastructure (electricity consumption and paved roads) on manufacturing sector performance in Nigeria for the period 1981-2019. The ex post facto research design was adopted for the study, using a combination of the traditional Ordinary Least Squares (OLS) econometric method, and the Augmented Dickey-Fuller unit root test for robust and valid estimation results. The co-integration test showed that the equations in the model were integrated. The study findings revealed that infrastructure, proxy by electricity supply had an adverse but not significant effect on manufacturing sector performance while paved roads had a positive and significant impact on manufacturing performance in Nigeria. The study recommends a total overhaul of the electricity sub-sector and targets increased supply specifically for industrial consumption as well as exploring alternative options to increase the road network such as public-private partnership arrangements given its importance to industrial performance.

Does High-Speed Rail Really Have a Positive Effect on City Consumption? A PSM-DID Approach With China Case

<p>This paper evaluates the treatment effect of high-speed rail (HSR) operation on city consumption using the dataset of Chinese cities from 2003 to 2019. Firstly, the applicability of observations is discussed; secondly, observations with no appropriate contrast samples are dropped for more precise empirical results. Then, propensity score matching (PSM) is implied to have the database much more balanced and suitable for the difference-in-difference (DID) framework, that is, the PSM-DID approach. The main results find a novel phenomenon of Simpson's paradox regarding the HSR-consumption nexus, which indicates that even though we can observe the positive effect on the whole, the results argue negative relationships between HSR and consumption within subclass cities. In addition, a dose-response assessment (DR) and some other checks have been proposed to demonstrate robust estimation results.</p>

Median robust nonlinear weighted total least squares estimator of nonlinear EIV models: three algorithms

To improve robust estimation performance of fully correlated nonlinear error-in-variables model, three median robust estimate methods are discussed on base of Nonlinear Weighted Total Least Squares (NWTLS), namely, median parameter method, median parameter initial value method and median variance method. After the discussion of the median robust estimation theory and the derivation of the calculation formula of the three median robust estimation methods, three iterative algorithms are developed. Through two nonlinear regression examples, four schemes including no artificial outliers, different location of outliers, different degrees of correlation, and different ratios of outliers are adopted, which proves the feasibility and effectiveness of the proposed algorithm. The experimental results show that from the statistical point of view, although the three median methods have different antioutlier effects for different nonlinear models, they are more effective than RNWTLS and NWTLS; the robust estimation results of three median methods are related to the functional model.

How the built environment promotes public transportation in Wuhan: A multiscale geographically weighted regression analysis

During rapid urbanization, the optimization of the built environment in metropolises to promote the use of public transportation considerably eases the road traffic pressure. Researchers usually adopt census or survey data and global models, which inadequately represent huge population and reflect the spatial non-stationarity. This study collected the location-based service data to identify commuters and their commuting modes and aggregated the share of transit commuting to traffic analysis zones (N = 586). While controlling three socio-economic attributes (age, gender & income), the 5Ds (Density, Diversity, Design, Distance to transit, and Destination accessibility) was formed to describe the built environment (BE) in Wuhan. Then we mainly compared the regression results of the geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). Although the GWR model could reveal the spatial non-stationarity by local fitting, its regression coefficients with fixed bandwidth (BW = 153) tended to show high variability with abnormal sign reversals due to overfitting. The application of MGWR model with adaptative bandwidths could further consider the scale effect and provide robust estimation results. According to the various bandwidths provided by MGWR, the policies to improve the BE had been discussed in three scales. We emphasize that policies should be formulated based on the spatial distribution of the coefficients, that is, “Adapt to local conditions”.

Application of Gibbs Sampling in Modelling the Utilization Rate of Raw Materials for Drug Coating

Drug coating is one of the most important processes in the modern pharmaceutical industry. Improving the utilization rate of raw materials (URRM) in the drug coating process is thus important for cost saving and efficiency enhancement. There is little existing research on this topic in the literature of applied statistics. In this paper, motivated by a real dataset collected from a pharmaceutical company in China, we propose to use a novel predictive model that integrates a Bayesian framework with the Gibbs sampling algorithm to characterize the pattern of URRM. Based on certain prior distributional assumptions, the Gibbs sampling algorithm is then applied to sample the posterior distribution of the parameters to obtain more accurate and robust estimation results. By using the proposed method, the drugs can be properly separated into several categories with different patterns of URRM, and the pattern of each category can be properly recognized with selected covariates, which achieves the goals of clustering, variable selection, and regression simultaneously, and provides valuable insights into the improvement of the URRM for drug coating. Numerical studies show that the proposed method works well in practice.

Day-of-the-Week Effect and Investors’ Psychological Mood Testing in a Highly Mispriced Capital Market

Research Aims: This research examines investors’ psychological moods which cause day-of-the-week anomalies in highly mispriced stock markets. Design/methodology/approach: We use a sample from the Indonesian capital market as, in the Asian region, this country is considered to have a highly mispriced capital market. We decompose the stock price index in Indonesia into speculative, less speculative, and non-speculative indexes. We employ the mean and variance regressions to control the heteroscedasticity and serial correlation. Novelties: Our novelties are two fold. We postulate a method to decompose stock price indexes in Indonesia (the JKSE, LQ 45, and Kompas 100) into speculative, less speculative, and non-speculative indexes. Secondly, we estimate the mean and variance levels simultaneously to get a robust estimation result of the anomaly. Research Findings: We empirically find that the behavior mood hypothesis is supported only during normal periods, when investors tend to be irrational and use their good mood to trade on speculative stocks on a Wednesday and sell them on Monday. In other periods, rationality and psychological effects play a role with Indonesian investors, when their mood is good they are more active in trading less speculative stocks, to avoid higher risks and earn higher returns from those less speculative and non-speculative stocks.

Parameter Identification and State Estimation of Lithium-Ion Batteries for Electric Vehicles with Vibration and Temperature Dynamics

There are different types of rechargeable batteries, but lithium-ion battery has proven to be superior due to its features including small size, more volumetric energy density, longer life, and low maintenance. However, lithium-ion batteries face safety issues as one of the common challenges in their development, necessitating research in this area. For the safe operation of lithium-ion batteries, state estimation is very significant and battery parameter identification is the core in battery state estimation. The battery management system for electric vehicle application must perform a few estimation tasks in real-time. Battery state estimation is defined by the battery model adopted and its accuracy impacts the accuracy of state estimation. The knowledge of the actual operating conditions of electric vehicles requires the application of an accurate battery model; for our research, we adopted the use of the dual extended Kalman filter and it demonstrated that it yields more accurate and robust state estimation results. Since no single battery model can satisfy all the requirements of battery estimation and parameter identification, the hybridization of battery models together with the introduction of internal sensors to batteries to measure battery internal reactions is very essential. Similarly, since the current battery models rarely consider the coupling effect of vibration and temperature dynamics on model parameters during state estimation, this research goal is to identify the battery parameters and then present the effect of the vibration and temperature dynamics in battery state estimation.

Integral Sliding-Mode Observer-Based Disturbance Estimation for Euler–Lagrangian Systems

In this article, a novel integral sliding-mode observer is proposed to estimate the external disturbance and velocity of Euler-Lagrangian systems. This method provides high bandwidth and precise estimation with only commanded input and position measurement. A system velocity measurement is not required to construct the sliding-mode manifold. The convergence of the estimation error to zero is theoretically in finite time, which is proven by a direct Lyapunov method utilizing the passivity property of Euler-Lagrangian systems. An integral sliding manifold is designed to reduce the reaching phase, such that the robustness of the estimation is enhanced. The method has been applied to a robot manipulator to estimate the joint velocity and external contact forces in a physical human-robot task. Simulations and experiments reveal that this novel method provides fast, precise, and robust estimation results and can be used to replace the measurement of an external force sensor. The successful application of this observer to a force-sensor-less admittance controller for a manipulator contributes to the implementation of a sensor-free safety framework for human-robot collaboration (HRC).

The Impact of Internet Use on Residents’ Subjective Well-Being: An Empirical Analysis Based on National Data

Internet use has become an indispensable part of people’s daily lives, and the relationship between Internet use and residents’ subjective well-being is receiving increasing attention from researchers. However, the existing literature on the relationship between Internet use and residents’ subjective well-being is still controversial and inconclusive, and there has been relatively little discussion of the mechanisms by which Internet use affects well-being. On the basis of data from the China General Social Survey 2015 (CGSS2015), this paper provides an empirical analysis of the impact of Internet usage on residents’ subjective well-being. The study finds that while Internet use had no significant impact on the well-being of residents compared to non-use, frequency of Internet usage did significantly improve subjective well-being. On this basis, we tested the mechanisms by which Internet use affects residents’ well-being through the three channels of the personal income effect, the human capital effect and the social capital effect. The findings partially verify the hypothesis of the mediating effect of social capital between Internet use and residents’ subjective well- being, but do not verify the hypothesis of the moderating effect of personal income and human capital. Compared with previous studies, this paper is innovative in the following three ways: it uses the latest available representative national microdata, which is better able to reflect recent Internet usage and its impact on residents’ subjective well-being; on the basis of existing research on the impact of Internet usage on residents’ well-being, it analyzes the specific mechanisms through which Internet use affects well-being, using the moderating effect model and the mediating effect model to make up for the shortcomings of existing research on the impact mechanism; and it applies the Bioprobit model to deal with the potential endogeneity of key explanatory variables, using a variety of model estimation methods to obtain more robust estimation results.

Point-Plane SLAM Using Supposed Planes for Indoor Environments.

Simultaneous localization and mapping (SLAM) is a fundamental problem for various applications. For indoor environments, planes are predominant features that are less affected by measurement noise. In this paper, we propose a novel point-plane SLAM system using RGB-D cameras. First, we extract feature points from RGB images and planes from depth images. Then plane correspondences in the global map can be found using their contours. Considering the limited size of real planes, we exploit constraints of plane edges. In general, a plane edge is an intersecting line of two perpendicular planes. Therefore, instead of line-based constraints, we calculate and generate supposed perpendicular planes from edge lines, resulting in more plane observations and constraints to reduce estimation errors. To exploit the orthogonal structure in indoor environments, we also add structural (parallel or perpendicular) constraints of planes. Finally, we construct a factor graph using all of these features. The cost functions are minimized to estimate camera poses and global map. We test our proposed system on public RGB-D benchmarks, demonstrating its robust and accurate pose estimation results, compared with other state-of-the-art SLAM systems.

Searching for the Next New Energy in Energy Transition: Comparing the Impacts of Economic Incentives on Local Acceptance of Fossil Fuels, Renewable, and Nuclear Energies

This study compares the impacts of economic incentives on attitudes related to the acceptance of fossil fuels, renewable, and nuclear energies. Linear and nonlinear regression models are applied for the robust estimation results. Empirical findings based upon these regression models are summarized as follows: First, when people belong to the upper social class, reside in metropolitan area, and have more trust in the government’s energy policy, they tend to accept the construction of specific energy-related facilities in a neighborhood and raise the probability of attitude change and stability. Second, those who have more perceived risk and negative image are less likely to accept any types of energies and tend to lower the probability of attitude change toward positive direction or stability. Third, those who have more knowledge are less likely to accept some energy sources such as fossil fuels and there exists a trade-off relationship between knowledge and trust. Finally, the structural changes between acceptance of all energy sources with and without economic incentives imply that economic incentives play a significant role in determining acceptance of energies.

Likelihood-based quantile mixed effects models for longitudinal data with multiple features via MCEM algorithm

In longitudinal data analysis, many data features are frequently encountered in practice. For example, variates are usually measured with censoring, substantial errors and non-normal feature, etc. In such case, it is impossible to give a precise result by conducting statistical inference of separate analysis. The joint modeling may be a considerable alternative. The superiority of joint modeling is that it can implement a simultaneous analysis for longitudinal mixed models with random effects, censoring, nor-normal errors, errors in covariates and heavy-tailed feature by pooling all data information together. However, most of traditional modeling methods aim at depicting the average variation of outcome variable conditionally on covariates, which may result in non-robust estimation results when suffering outliers or non-normal errors. Quantile regression provides an attractive alternative to model longitudinal data with multiple data features, which can draw a complete picture of the conditional distributions of outcome variable and bring out robust estimation results. In this paper, we conduct the likelihood-based joint quantile regression for longitudinal mixed models by accounting for the above multiple data features simultaneously. Based on the asymmetric Laplace distribution (ALD), the Monte Carlo Expectation-Maximization (MCEM) algorithm is employed to address the estimation problem. Finally, some Monte Carlo simulations are conducted and an AIDS data analysis is provided to illustrate the developed procedures.

State-of-charge estimation approach of lithium-ion batteries using an improved extended Kalman filter

The extended Kalman filter (EKF) algorithm is generally employed to track SOC rapidly due to the linearization process. However, the traditional EKF suffers from divergence since the inappropriate predetermined noise information. To provide robust estimation results, a novel estimation framework based on the battery model and the improved EKF algorithm is proposed. Firstly, the Akaike information criterion is employed to establish the optimal open circuit voltage-SOC model to improve model accuracy. Secondly, a two-stage estimation algorithm is proposed to improve the robustness of estimation and reduce calculation simultaneously. The joint recursive least square (RLS) algorithm and EKF algorithm are firstly employed for online parameter identification and coarse-grained estimation to track SOC rapidly. Then, the particle swarm optimization algorithm is introduced for fine-grained adjustment to find the global optimal estimation value. Experiments are performed on the lithium-ion batteries to verify the effectiveness of the proposed method. The results indicate that the proposed method can provide a more accurate and robust estimation result and better convergent time than the EKF.

A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System

With the increasing importance of renewable energy and flexible loads, the operation of the distribution system is becoming more stochastic and complex, and it is necessary to monitor the power system in real-time. Considering the gradual applications of intelligent electronic devices in the distribution systems, a hybrid state estimator based on supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements is proposed in this paper, which consists of the improved robust estimation and linear state estimation. At the time of SCADA data acquisition, the improved robust estimation combining the SCADA measurements with PMU measurements is performed. To eliminate the effect of bad data, the internal student residual method is introduced, and the robust thresholds are adjusted adaptively. Then the linear state estimation is performed at the time of PMU data acquisition based on the results of the previous estimation time and the PMU measurements, which can quickly correct the robust estimation results and track the changes of the distribution system. Finally, the effectiveness and performance of the proposed method are verified in a modified IEEE 33-bus distribution system and a real distribution system in China.

Generalized Content-Preserving Warp: Direct Photometric Alignment Beyond Color Consistency

Motion estimation is vital in many computer vision applications. Most existing methods require high quality and large quantity of feature correspondence and may fail for images with few textures. In this paper, a photometric alignment method is proposed to obtain better motion estimation result. Since the adopted photometric constraints are usually limited to the required illumination or color consistency assumption, a new generalized content-preserving warp (GCPW) framework, therefore, is designed to perform photometric alignment beyond color consistency. Similar to conventional content-preserving warp (CPW), GCPW is also a mesh-based framework, but it extends CPW by appending a local color transformation model for every mesh quad, which expresses the color transformation from a source image to a target image within the quad. Motion-related mesh vertexes and color-related mapping parameters are optimized jointly in GCPW to get more robust motion estimation results. Evaluation of tens of videos reveals that the proposed method achieves more accurate motion estimation results. More importantly, it is robust to significant color variation. Besides, this paper explores the performance of GCPW in two popular computer vision applications: image stitching and video stabilization. Experimental results demonstrate GCPW’s effectiveness in dealing with typical challenging scenes for these two applications.

An Enhanced Deep Extreme Learning Machine for Integrated Modular Avionics Health State Estimation

Integrated modular avionics (IMA) is one of the most advanced systems whose performance deeply impact on the security of civil aircraft. In order to enhance the safety and reliability of aircraft, the health state of the IMA must be estimated accurately. Since IMA is a real-time system, the estimation algorithm should have fast learning speed to satisfy the real-time requirement. In this paper, an enhanced deep extreme learning machine is developed to estimate the health states of IMA. First, the enhanced deep extreme learning machine is built in a novel fashion by using a dropout technique and extreme learning machine autoencoder. Second, multiple-enhanced deep extreme learning machines with different activation functions are employed to estimate the health states, simultaneously. Finally, a synthesis strategy is designed to combine all the results of different enhanced deep extreme learning machines. In such a manner, the robust and accurate estimation results can be obtained. In order to collect the data under different health states, a performance degradation model of IMA is built by the intermittent faults. The proposed method is applied to health state estimation, and the results confirm that the proposed method can present a superior estimation to the conventional methods.