Measurement Error Problems Research Articles

IMPLEMENTASI ALGORITMA K-MEDOIDS DAN K-ERROR UNTUK PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN JUMLAH PRODUKSI PETERNAKAN TAHUN 2020

The livestock sub-sector is one of the sub-sectors that contribute to the national economy and can significantly absorb labour so that it can be relied upon in efforts to improve the national economy. One of the steps used to increase livestock production in each region in Central Java Province is regional mapping. Cluster analysis is one of the regional mapping methods that can increase livestock production by grouping regencies/cities with characteristics of the same level of livestock production based on the type of livestock production. The k-error and k-medoids method is a non-hierarchical cluster analysis method, where the k-error is a method developed to overcome the problem of data measurement errors in classical cluster analysis, while the k-medoids is a method used to overcome the problem of outliers contained in the data. The validity test of the standard deviation ratio and the WB Index was used to determine the quality of the clustering results. The small validity value of the standard deviation ratio and the WB Index shows the best results of clustering and selecting method. Based on the results of the clustering, the optimal cluster was obtained at k=7 using the k-medoids algorithm, where the validation value of the standard deviation ratio=0.773 and WB Index=0.531.

Small area estimation with multiple covariates under structural measurement error models

Small area estimation is essential in making reliable inferences for areas where the sample is relatively small or no sample is available. In the case of developing a small area estimation model, if the covariates measured with errors were ignored, the estimation results may be worse than the direct estimate. Therefore, the problem of measurement error in covariates imposes challenges to data analytics in small area estimation. This paper studies a model with multiple covariates subject to structural measurement error and multiple error-free covariates. This study explores different scenarios in a simulation study by creating a finite super population spread across the area, investigating the sensitivity to sample size, and determining the variance of measurement error that was lower than the variance of the sampling error and the variance of the random effect. Jackknife method was employed to obtain a nearly unbiased estimator of the mean squared prediction error of empirical best predictors of the predictors of small area means. The research shows that the performance of the empirical best predictors is better in the case of a large sample size within the area and small variances of the measurement errors. The weighted version is recommended because it is more stable in terms of variability and bias.

Growth Regression Revisited: Selection and Conditional Convergence

Baumol (1986)’s growth regression is a starting point in growth empirics. But, as Delong (1988) pointed out, it has serious defect of sample selection and measurement error. These can lead to inconsistency of the estimate of convergence parameter. After their studies, numourous econometric studies have been performed. I incorporate these methods into growth regression framework. In this study, I apply Heckit (1979) to this regression using Peen World Table10 panel data in addition to the same data of Baumol’s. I extend the econometric model also using instrumental variable estimation for solving measurement error problem. As Delong (1988) derived similar way, correcting it with Heckit (1976) and maximum likelihood methods reduce (near) no convergence. His solution seems to be incomplete, in that the selection of countries by Baumol (1986) is not explicitly estimated. In general, the biasedness and inconsistency in growth regression come from more selection rather than correlation of explanatory variables with error such as measurement error or dynamic panel regression.

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy uses predictive modeling techniques to “mine” variables of interest from available data and then includes those variables into an econometric framework to estimate causal effects. However, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables likely suffer from bias due to measurement error. We propose a novel approach to mitigate these biases, leveraging the random forest technique. We propose using random forest not just for prediction but also for generating instrumental variables for bias correction. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make “different” mistakes, that is, have weakly correlated prediction errors. A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the others serve as its instruments. Simulation experiments demonstrate its efficacy in mitigating estimation biases and its superior performance over alternative methods. History: David Martens served as the senior editor for this article. Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/7039927/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2022.0019 ).

RDTS Noise Reduction Method Based on ICEEMDAN-FE-WSTD

Aiming at the problem of temperature measurement error caused by the noise signal on the Raman-based distributed optical fiber temperature sensor (RDTS), a noise reduction method based on the improved complete ensemble empirical mode decomposition with adaptive noise, fuzzy entropy, and the wavelet soft threshold denoising (ICEEMDAN-FE-WSTD) is proposed. First, ICEEMDAN is used to decompose the input signal, and noise intrinsic mode functions (IMFs) are determined by calculating the FE value of each IMF. Then, noise IMFs are denoised by the WSTD method. Finally, the noise reduction results are obtained by combining the denoised IMFs with the remaining useful signal IMFs for reconstruction. The advantages of the proposed method in terms of denoising and character extraction are verified by the simulation signal experiment with different signal-to-noise ratios (SNRs). The temperature measurement experiments are carried out with heating areas set at 40 °C, 45 °C, 50 °C, 55 °C, and 60 °C. Taking the 40 °C temperature measurement experiment as an example, after the temperature measurement signal is denoised by the proposed method, the maximum temperature measurement error in the heating area is reduced by 1.07 °C, the SNR is increased by 4.83 dB, and the root-mean-square error (RMSE) is reduced by 0.77. The temperature drift of RDTS signals after noise reduction by the proposed method is also effectively suppressed, and the maximum temperature measurement error at 2.28 km is reduced by 1.43 °C. The denoising results of the proposed method are compared with the other six noise reduction methods in this article, and the proposed method has the best performance.

A standardized method for the quantification of polysaccharides: An example of polysaccharides from Tremella fuciformis

The most widely used colorimetric method based on the phenol-sulfuric acid system generally has problems of poor reproducibilities and large measurement errors. In this study, bioactive Tremella fuciformis polysaccharides (TFPS) were used as the test materials, a systematic investigation was performed to solve these problems. Influences of temperature, TFPS hydrolysis, enzyme interferences, and other factors affecting the TFPS determination were intensively studied and optimized. The accurate quantification of TFPS in the presence of enzyme interference was also innovatively realized by the establishment of corrected calibration standard curves. In addition, long-term storage conditions of TFPS samples at different temperatures and storage time were also addressed and compared. This study, for the first time, completely solved the problems that existed in traditional phenol-sulfuric acid methods and thereby provided a certain theoretical basis not only for accurate detection and quantification of TFPS but for standardization and further utilization of other types of polysaccharides as well.

Lateral distance measurement technology of driverless vehicle based on machine vision

In order to solve the problems of large measurement errors and long time consuming in the measurement of the lateral distance between driverless vehicles, this paper proposes to design a lateral distance measurement technology of driverless vehicles based on machine vision. According to the preview following theory and model basis, the lateral movement process of driverless vehicles is determined, the lateral displacement deviation change rate and heading angle deviation change rate between vehicle and preview point in local coordinates are calculated, and the lateral motion dynamic model of unmanned vehicle is designed. With the help of Yolo algorithm, the boundary frame is drawn up to determine the position of the lateral moving vehicle, and the detection error is corrected with the help of loss function to complete the lateral moving target detection of the unmanned vehicle. The relationship between the machine coordinate system and the world coordinate system is determined. According to the internal and external parameter matrix of the relationship between the lateral distances of the driverless vehicle, the machine vision position is set, and the lateral distance is determined by the pitch angle between the horizontal planes. The experimental results show that the measurement error of this method is always less than 5%. This method has small measurement error, small error between test distance and actual distance, and has certain application performance.

Multitemporal Change Detection and Irregular Land Shape Area Measurement from Multispectral Sensor Images through BSO Algorithm

Pixel-based classification and area measurement play a vital role in satellite image processing. The accuracy in classification and area measurement is required for remote monitoring various regions such as water area, cultivation regions, reservoir water spread, and disease spread in cultivation area. Traditionally, Google Earth Pro-based area measurement has error in area measurement due to curvature nature and irregular land surface. Moreover, exact point identification on land surface on Earth pro is difficult due to frequent changes on the land surface. The problem in the land area measurement is mostly affected due to the man-made changes in that particular land area. In this paper, we solve land area measurement error problem through the automated single and multithresholding pixels of different iterations on the land surface by the BSO algorithm. The land cover region pixel intensity changes on the curvature region of land surface with respect to spatial and temporal variations which are identified through BSO optimization-based image segmentation for exact area measurement. For the experimentation of accurate measurement, land area images such as urban, semiurban, hill, and coastal region from LANDSAT and SENTINEL images for period 2016 to 2019 are taken for the land area measurement study. BSO enhances and segments the land regions such as road, building, water body, vegetation, bare land, hill, and coastal region of about 32% more than particle swarm optimization (PSO) algorithm. Furthermore, the urban land area measurement accuracy increases to about 97% than the irregular land surface area.

The dynamic analysis of renewable energy's contribution to the dimensions of sustainable development and energy security.

In sustainable development, energy is critical in human activities and shapes a sustainable future. Thus, it is an unignorable element in human development. This paper analyzes the contributions of renewable energy sources (RES)'s to the economic, environmental, and social dimensions of sustainable development. Moreover, we add energy security as a possible fourth dimension into the analysis. For the sample size, we limit the countries members of the OECD and run generalized methods of moments for the period from 1995 to 2015. This method can produce efficient estimators under the problems of endogeneity, omitted-variable bias, measurement errors, and heteroscedastic residuals. According to the results, RES has a small reducing effect (-.007%) on output in the Cobb-Douglas production function for the economic dimension. We found that RES has a positive contribution to the environmental dimension and abates the level of carbon emission (-.093%). RES also confirms the inverted-U shape of environmental Kuznets curve. In the social dimension, RES improves human development and a 1% increase in RES consumption causes to .0045% increase in human development. In the last contribution, RES has a positive effect on sustainable energy supply security in the context of electricity generation (.032%). Although the effects of RES on the environment, social, and energy security are significant, they are limited. These limitations point to barriers that can be overcome over time. Our conclusions recommend that these effects might flourish with technical developments and political support in the long run. Furthermore, public awareness, rising income level, and economies of scale are also beneficial in this process. As a result, RES might be an excellent source for a sustainable future and development. Especially, RES might have remarkable contributions to the 7th, 11th, 12th, and 13th goals of sustainable development.

Reflections on an emerging new science of mental disorders

This article reflects on the papers included in the Special Issue on The Network Approach and the Future of Clinical Psychology (Roefs, Jansen, & Fried, 2022). The emergence of major empirical research programs into symptom networks is discussed in relation to the research agenda involving the system dynamics of mental disorders. This research agenda is then related to major themes addressed in the special issue: the integration of empirical results into a larger theoretical framework, the role of mental representations and neurobiological factors, the problem of measurement error, and the presence of heterogeneity. Taken together, the generation of large scale research programs, the availability of new sources of data, and the availability of network approaches creates momentum that may allow us to take crucial steps forward in assessing, modeling, and intervening on mental disorders.

Asset pricing models with measurement error problems: A new framework with Compact Genetic Algorithms

Asset pricing models with measurement error problems: A new framework with Compact Genetic Algorithms

Economic consequences of bargain purchase gains under ASC 805: evidence from banking industry

ABSTRACT I examine whether bargain purchase gains (previously known as negative goodwill) reported under FASB’s Accounting Standards Codification 805, Business Combinations (ASC 805) in FDIC-assisted transactions represent economic gains to the acquiring banks and thereby qualify as part of banks’ Tier 1 capital. In a sample of 135 publicly-held U.S. banks that acquired failed banks in the period January 2009-April 2012, I find that bargain purchase gains are positively associated with acquirers’ future stock market and operating performance. Also, bargain purchase gains appear to capture expected future gains, including and beyond the FDIC subsidy. Specifically, the component of bargain purchase gains that is less likely related to the FDIC subsidy is positively associated with acquirers’ future short-run and long-run stock market performance and accounting operating performance. Finally, the factors, such as agency problem, internal control strength, and corporate governance strength, that affect the reliability of financial reporting matter for the reliability of bargain purchase gain estimation. Overall, the results imply that fair value accounting as implemented under ASC 805 enables bargain purchase gains to reliably reflect expected future economic gains, rather than measurement error or agency problem.

Self-disclosed peer effects on corporate capital structure

• New approach to assessing the impact of peers on corporate capital structure. • Use actual company peer disclosures to create a directed network. • The approach recognizes that firms’ lines of business and peers change through time. • Robust peer effects are found when rivals share at least one customer with the firm. We study a network of interconnected firms and examine the impact of the firm’s business relationships with peers, rivals, and customers on its capital structure. Peer effect models commonly define peers based on three- or four-digit Standard Industrial Classification (SIC) codes. This renders them susceptible to measurement error and identification problems. These issues are of consequence, since we show that: a) many firms change industry affiliations over time and b) over one-half of peers revealed by managers to shareholders in a given year reside in industries that differ from the firm’s static of historical SIC code. We find that peer effects on financial policy are robust when the firm’s revealed peer group consists of self-disclosed rivals that share at least one customer with the firm in a two-year time window.

Robust statistical tools for identifying multiple stellar populations in globular clusters in the presence of measurement errors

Context.The finding of multiple stellar populations (MPs), which are defined by patterns in the stellar element abundances, is considered today a distinctive feature of globular clusters. However, while data availability and quality have improved in the past decades, this is not always true for the techniques that are adopted to analyse them, which creates problems of objectivity for the claims and reproducibility.Aims.Using NGC 2808 as test case, we show the use of well-established statistical clustering methods. We focus our analysis on the red giant branch phase, where two data sets are available in the recent literature for low- and high-resolution spectroscopy.Methods.We adopted hierarchical clustering and partition methods. We explicitly addressed the usually neglected problem of measurement errors, for which we relied on techniques that were recently introduced in the statistical literature. The results of the clustering algorithms were subjected to a silhouette width analysis to compare the performance of the split into different numbers of MPs.Results.For both data sets the results of the statistical pipeline are at odds with those reported in the literature. Two MPs are detected for both data sets, while the literature reports five and four MPs from high- and low-resolution spectroscopy, respectively. The silhouette analysis suggests that the population substructure is reliable for high-resolution spectroscopy data, while the actual existence of MP is questionable for the low-resolution spectroscopy data. The discrepancy with literature claims can be explained with the different methods that were adopted to characterise MPs. By means of Monte Carlo simulations and multimodality statistical tests, we show that the often adopted study of the histogram of the differences in some key elements is prone to multiple false-positive findings.Conclusions.The adoption of statistically grounded methods, which adopt all the available information to split the data into subsets and explicitly address the problem of data uncertainty, is of paramount importance to present more robust and reproducible research.

A new robust parameter estimation approach for multinomial categorical response data with outliers and mismeasured covariates

Being confronted with multinomial categorical response data often occurs in the medical, health, social sciences and other fields. However, the data in practice are subject to measurement error (ME) or outliers due to some unavoidable reasons. There are few studies dealing with problems of ME and outliers in the data with unordered and multi-classified responses simultaneously. To address the effects of ME and outliers in multinomial logistic models, which are most suitable for analyzing categorical outcome variables, we propose novel approaches. Firstly, we develop the extensively weighed corrected score method to estimate parameters in multinomial logistic models with ME. Furthermore, to tackle the problem of that how to obtain parameter estimations when the ME and outliers occur simultaneously, we develop a new robust method, namely the robust extensive weighted corrected score method. Some simulation studies are conducted to evaluate the estimators’ finite sample performance. It has been verified that these estimators are consistent and asymptotically normally distributed under general conditions. They are easy to compute, perform well and stably and are robust against the normality assumption for the ME. The proposed methods are also applied to two real datasets to illustrate the practicability of methods.

Relative Position and Posture Measurement Using Projected Markers

In recent years, demand for UAVs has been increasing in various situations such as aerial photography, infrastructure inspections of bridges, and logistics. In this study, we focus on bridge inspections in non-GPS environments and propose a measurement method using a projected marker and camera that enables self-position estimation. This measurement method aims to solve the problem of the large measurement error of position and posture as the shooting distance increases by using "laser parallelism correction". Experiments were conducted using a measurement unit consisting of a laser pointer, a camera, and a projection plane, and image processing software was used to analyze the captured images. The experiment was conducted by measuring the position of the projection plane at several shooting distances and changing the posture of the projection plane at each shooting distance to measure the posture. As a result, we succeeded in reducing the measurement error, which had been a problem in previous studies, by using laser parallelism correction, and confirmed the effectiveness of this method.

Kalman-Filtering-Based Frequency Control Strategy Considering Electrolytic Aluminum Load

Traditional thermal power units are continuously replaced by renewable energies, of which fluctuations and intermittence impose pressure on the frequency stability of the power system. Electrolytic aluminum load (EAL) accounts for large amount of the local electric loads in some areas. The participation of EAL in local frequency control has huge application prospects. However, the controller design of EAL is difficult due to the measurement noise of the system frequency and the nonlinear dynamics of the EAL’s electric power consumption. Focusing on this problem, this paper proposes a control strategy for EAL to participate in the frequency control. For the controller design of the EAL system, the system frequency response model is established and the EAL transfer function model is developed based on the equivalent circuit of EAL. For the problem of load-side frequency measurement error, the frequency estimation method based on Kalman-filtering is designed. To improve the performance of EAL in the frequency control, a fuzzy EAL controller is designed. The testing examples show that the designed Kalman-filter has good performance in de-noising the measured frequency, and the designed fuzzy controller has better performance in stabilizing system frequency than traditional methods.

Selection of Characteristic Particle Size of Drilling Cuttings Based on Adsorption-Desorption Properties: Experiment and Simulation

The direct method for measuring gas content based on image processing, that is, comparing the desorption curves of drilling cuttings with the characteristic particle size with the desorption image in the database to determine the gas content of coal seams, can solve the problems of long measurement time and larger measurement errors of the traditional direct method. However, the precondition to realize this method is to define the characteristic particle size of drilling cuttings. Therefore, this paper performed adsorption and desorption experiments under different particle size ranges and dissimilar adsorption equilibrium pressures, solved the gas migration control equation in coal particles, and determined the characteristic particle size from experimental results and simulation effect. The results showed that gas desorption quantity was positively correlated with adsorption equilibrium pressure, while negatively correlated with the size of a particle size range. The greater the adsorption equilibrium pressure, the better the linear fitting relationship with the maximum desorption quantity, and the maximum correlation coefficient could reach 0.9658. Based on Darcy’s law, Langmuir adsorption equation, and mass conservation law, the theoretical control equation of gas migration in coal particles was derived and solved by Open source Field Operation and Manipulation (OpenFOAM). The accuracy of the calculation results of the solver was verified by the derived analytical solution of the gas pressure change of coal particles without considering the adsorption. From the experimental results and simulation results, the characteristic particle size of drilling cuttings was determined to be 0.5-1 mm. The research can provide help for the subsequent development of direct gas content measurement technology based on image processing.

Multiplication sampling moire method for full-field deformation measurement of composite materials.

A multiplication sampling moire (MSM) method was proposed for robust deformation distribution measurement by performing phase analysis of the second harmonic (second-order frequency) of a single grating pattern. The MSM method has a very strong noise immunity because the second harmonic spectrum is far from the low-frequency region of the background noise in the frequency domain. Phase analysis of an experimental grid image on a carbon fiber-reinforced plastic (CFRP) specimen indicated that the MSM method effectively solved the problem of non-negligible phase measurement errors of conventional methods that extract the fundamental frequency of the grating, in the case of severe local noise. The displacement and strain distributions of CFRP in a tensile test were successfully measured. This method is suitable for deformation measurement of various composite materials.

How serious is the measurement-error problem in risk-aversion tasks?

How serious is the measurement-error problem in risk-aversion tasks?