Data Estimation Methods Research Articles

Estimation of Arc Welding Pressure Pipeline Weld Peaking Parameters Based on Data Prediction

Peaking parameter is the key content in the regular inspection of the pressure pipeline. Solving the problem of the peaking measurement method defined by a standard cannot be applied to a situation in which there exists a weld surface with reinforcement and misalignment. In this paper, a peaking estimation method based on data prediction was proposed to estimate the contour information of the base metal at the weld joint using the contour point set data of the base metal part of the weld. Herein, the longitudinal weld peaking estimation method based on a piecewise logistic regression (PLR) and the girth weld peaking estimation method based on a piecewise Bayesian linear regression (PBLR) were studied, and the midpoint of the two symmetrical points of the base metal on either side of the weld was used as a reference for calculating the peaking. Finally, we collected the surface profile data of longitudinal weld pressure pipes with diameters of 155 mm, 255 mm, 550 mm, and 600 mm and the surface profile data of girth weld pressure pipes with diameters of 120 mm, 130 mm, 140 mm, and 170 mm. This weld seam data used the data estimation method proposed in this article and traditional long short-term memory and fitting methods. The results showed that the proposed data prediction method could accurately predict the position of the base metal, and the theoretical mean absolute error of the peaking estimation based on the PBLR and PLR could attain 0.06 mm and 0.07 mm, respectively, which meets the parameter measurement requirements of related verification fields.

The Impacts of Public Administrative Quality on Profitable Enterprises in Vietnam

This study aims to understand the relationship between the quality of public administration and the rate of profitable enterprises by locality in Vietnam in the period 2015 - 2019. With a combined data set from two sources including The first is the Provincial Competitiveness Index (PCI) dataset jointly implemented by the Vietnam Chamber of Commerce and Industry (VCCI) with the support of the United States Agency for International Development (USAID) in Vietnam, the second is the "White Book of Vietnamese Enterprises" compiled and published annually by the General Statistics Office of Vietnam. Combined with panel data estimation method to assess the impact of public administration quality indicators on the rate of profitable enterprises by locality. Research results show that there exists a relationship between the quality of public administration and the rate of profitable enterprises. Especially the indicators of Entry Costs, Policy Bias, and Labor and Training have both positive and negative effects with very strong statistical significance.

Environmental implications of economic complexity and its role in determining how renewable energies affect CO2 emissions

The economic complexity index's environmental implications have been thoroughly investigated in this study. In particular, an effort is made to determine the index's utility in explaining and filling in the gaps left by experimental studies on the effects of renewable energy on CO2 emissions. The study sampled 29 Asia-Pacific countries over the period 2000–2018. The relationship between the complexity index and CO2 emissions has been investigated using panel data models and estimation methods. According to the findings, increasing the intensity of energy use, trade openness, and urbanization all increase CO2 emissions, although the effects of urbanization eventually became insignificant in the long run. Furthermore, while the logarithm of GDP per capita’s coefficients for the level and squared forms confirmed the Environmental Kuznets Curve (EKC) hypothesis, the economic complexity index shifted the curve. The results indicate that economic complexity in countries enhances economic growth effects to increase CO2 emissions through increased production scale and energy demand. On the other hand, increasing economic complexity through increased energy efficiency demonstrates its beneficial effects on the environment. According to the findings, increasing wind and solar energy share has reduced CO2 emissions in countries with lower economic complexity. In comparison, in more complex countries along with these substitution effects, there is also a scale effect associated with increasing renewable energy use, which eventually results in increased CO2 emissions.

Evaluation of Egyptian Foreign Trade with the Most Important Partners in Light of the Change in the Exchange Rate

Both exports and imports are one of the important strategic axes of Egypt's economic development plan, Exports are an important source for foreign exchange, and imports are a source of goods that domestic production cannot cover. And According to the direct correlation and the impact of both exports and imports on the Egyptian economy, and According to the importance of the continuous and periodic evaluation of foreign trade to highlight the most important effects of the surrounding changes on it, The research problem is based on an assessment of Egyptian trade with the most important partners in light of the exchange rate change and in the form of the following question: Are exports and imports between Egypt and its most important partners reaching their desired levels? Does the exchange rate have an effect on it? So that the objective is to compare the actual situation with the forecast for both exports and imports, whether at the country level or for the average years for the two periods (2013-2015, 2017-2019) (Before and after exchange rate change). The research was based on data for 20 countries in the case of exports, 33 states in the case of imports as the most important business partners with Egypt, the gravity model has been used with the use of the estimation method for mixed data and the use of the Hausman test to prefer between the two random and fixed effects models.

Twin deficits revisited: A role for fiscal institutions?

We revisit the twin-deficits relationship for a sample of 65 countries with fiscal rules over the period 1985–2015, using a panel data estimation methods. Our analysis accounts for the role of various types of fiscal rules and institutions: expenditure rules, revenue rules, budget balance rules, debt rules, fiscal councils, and supporting procedures. We find that the twin-deficits hypothesis is confirmed. The impact of the budget balance on the current account balance is increased when fiscal rules are considered, except with revenue rules and debt rules. Well-designed fiscal rules, fiscal councils and features that reinforce compliance with rules improve the current account balance. Our findings highlight the role of fiscal factors in explaining sustained global current account imbalances. They also contribute to the ongoing discussion about the improvement of macroeconomic and budgetary surveillance in the European context.

The Effectiveness of China's Foreign Trade Liberalization

Abstract This paper studies the ex-post trade effects of China's multilateral, regional and bilateral trade liberalization using augmented gravity and panel data estimation methods for the period 1995–2016. China's accession to the WTO was revealed to have had a significant impact on the volume of trade as well as on bilateral exports and imports. Regional trade agreements and the majority of bilateral trade agreements were found not to be effective in increasing China's foreign trade. Only the agreements with Chile, Costa Rica and Switzerland were effective in increasing China's trade volume. Moreover, the results for Chile were driven by increases in both exports and imports, while for Costa Rica and Switzerland only by increased imports from these countries.

Jewel: A Novel Method for Joint Estimation of Gaussian Graphical Models

In this paper, we consider the problem of estimating multiple Gaussian Graphical Models from high-dimensional datasets. We assume that these datasets are sampled from different distributions with the same conditional independence structure, but not the same precision matrix. We propose jewel, a joint data estimation method that uses a node-wise penalized regression approach. In particular, jewel uses a group Lasso penalty to simultaneously guarantee the resulting adjacency matrix’s symmetry and the graphs’ joint learning. We solve the minimization problem using the group descend algorithm and propose two procedures for estimating the regularization parameter. Furthermore, we establish the estimator’s consistency property. Finally, we illustrate our estimator’s performance through simulated and real data examples on gene regulatory networks.

A Weight Assignment Algorithm for Incomplete Traffic Information Road Based on Fuzzy Random Forest Method

One of the keys in time-dependent routing is determining the weight for each road network link based on symmetrical and complete traffic information. To facilitate travel planning considering traffic situations based on historical global position system (GPS) trajectory data which uncover the whole road network, this paper proposes a fuzzy random forest-based road section data estimation method, which uses the third law of geography as the core idea. For different time periods, road grade, tidal lane, proximity to infrastructure (main places that affect traffic, such as schools, hospitals), and accident road sections were selected as indicators that influence the traffic. The random forest algorithm is used to build the mapping relationship between attribute data with average traffic which is obtained based on GPS data. Subsequently, the fuzzy reasoning method is used to obtain the weight of road links missing traffic information by calculating their similarities with typical road section samples. Using the road network of Suzhou City as an example, the proposed method was used to analyze estimate the average driving speeds of road sections with missing traffic information for different time periods. Experimental results show that this method can effectively avoid congested road sections and obtain high-speed travel routes.

Using a tier classification system to evaluate the quality of bycatch estimates from fisheries

Bycatch continues to be a challenge to sustainable fisheries management (The term “bycatch” in this paper covers discards and does not include retained incidental catch). Bycatch estimates can inform stock status determinations by improving understanding of fishing mortality, and help managers monitor the effectiveness of regulations. Assessments of the quality of bycatch estimation programs and procedures are necessary to evaluate the precision and limitations of their results over time. NOAA Fisheries experts used a “Tier Classification System” (TCS) to compare the quality of fish bycatch data and estimation methods for U.S. commercial fisheries in 2005 and 2015. The TCS included criteria related to data adequacy and analytical approaches. A comparison of U.S. fishery tier scores demonstrated that most fisheries were classified into higher tiers in 2015 compared to 2005 due to factors including improved sampling design. In addition, this comparison identified region-specific trends (e.g., mostly improvements occurred for Alaska fisheries with more mixed results for Greater Atlantic fisheries). The improvements in bycatch data quality and estimation methods in the United States are a result of financial investments in observer programs by NOAA Fisheries and industry partners, as well as effective conservation measures implemented by regional fishery management councils and NOAA Fisheries. The TCS was also used to assess bycatch data and estimation methods in all of Australia’s fishery jurisdictions for the decade 2010–19, illustrating the international applicability of the method. Overall, Australian state fisheries scored lower than federally managed fisheries in both the United States and Australia, reflecting the fact that the latter fisheries tend to be larger (and more valuable) than those in state jurisdictions, with a larger investment in observer programs. A comparison of tier scores and estimates of discards by fishery may provide a useful input for decision-making processes regarding allocation of resources to improve bycatch monitoring.

Relationship Between Commercial Bank Availability and Income Inequality

The objective of this study is to examine the relationship between commercial bank branches availability and income inequality. Further, the study also assesses the interaction effect of corruption and commercial bank availability on income inequality. The present study uses panel data estimation methods for analysing the above relationship for SAARC countries (Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka). The analysis suggests that a positive relationship exists between income inequality and financial availability in the initial stages. However, as the financial institutions reach a level of maturity and more people are integrated in the financial network, the level of income inequality starts reducing. Moreover, increase in financial availability helps in reducing income inequality when it is supported by less corrupt institutions. Policymakers should focus on reducing the level of corruption so as to enhance the effectiveness of the penetration of commercial bank branches.

Relationship Between Commercial Bank Availability and Income Inequality

The objective of this study is to examine the relationship between commercial bank branches availability and income inequality. Further the study also assesses the interaction effect of corruption and commercial bank availability on income inequality. The present study uses panel data estimation methods for analysing the above relationship for SAARC countries (Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka). The analysis suggests that a positive relationship exists between income inequality and financial availability in the initial stages. However, as the financial institutions reach a level of maturity and more people are integrated in the financial network, the level of income inequality starts reducing. Moreover, increase in financial availability helps in reducing income inequality when it is supported by less corrupt institutions. Policymakers should focus on reducing the level of corruption so as to enhance the effectiveness of the penetration of commercial bank branches.

RELATIONSHIP BETWEEN COMMERCIAL BANK AVAILABILITY AND INCOME INEQUALITY - EVIDENCE FROM SAARC COUNTRIES

The objective of this study is to examine the relationship between commercial bank branches availability and income inequality. Further the study also assesses the interaction effect of corruption and commercial bank availability on income inequality. The present study uses panel data estimation methods for analysing the above relationship for SAARC countries (Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka). The analysis suggests that a positive relationship exists between income inequality and financial availability in the initial stages. However, as the financial institutions reach a level of maturity and more people are integrated in the financial network, the level of income inequality starts reducing. Moreover, increase in financial availability helps in reducing income inequality when it is supported by less corrupt institutions. Policymakers should focus on reducing the level of corruption so as to enhance the effectiveness of the penetration of commercial bank branches.

Is industrial pollution detrimental to public health? Evidence from the world\u2019s most industrialised countries

BackgroundIndustrial pollution is considered to be a detrimental factor for human health. This study, therefore, explores the link between health status and industrial pollution for the top 20 industrialised countries of the world.MethodsCrude death rate is used to represent health status and CO2 emissions from manufacturing industries and construction, and nitrous oxide emissions are considered to be indicators of industrial pollution. Using annual data of 60 years (1960–2019), an unbalanced panel data estimation method is followed where (Driscoll, J. C. et al. Rev Econ Stat, 80, 549–560, 1998) standard error technique is employed to deal with heteroscedasticity, autocorrelation and cross-sectional dependence problems.ResultsThe research findings indicate that industrial pollution arising from both variables has a detrimental impact on human health and significantly increases the death rate, while an increase in economic growth, number of physicians, urbanisation, sanitation facilities and schooling decreases the death rate.ConclusionsTherefore, minimisation of industrial pollution should be the topmost policy agenda in these countries. All the findings are consistent theoretically, and have empirical implications as well. The policy implication of this study is that the mitigation of industrial pollution, considering other pertinent factors, should be addressed appropriately by enunciating effective policies to reduce the human death rate and improve health status in the studied panel countries.

Las tecnologías de la información y la comunicación como determinantes en el modelo de crecimiento de Corea del Sur: perspectivas hacia una alianza comercial con México

A pesar de la ausencia de un acuerdo comercial bilateral entre Corea del Sur y México, una de las motivaciones que alienta esta formalización es el desarrollo acelerado que han tenido las tecnologías de la información y la comunicación (tic) en el cre-cimiento económico coreano, pues han contribuido en la administración y organización de la industria, en la estandarización del comercio y la educación, entre otros rubros. El objetivo del estudio es ana-lizar el impacto de las tic como determinantes en el modelo de crecimiento económico de Corea del Sur dentro de un contexto regional que permita también detectar efectos particulares. Para ello se emplea un método de estimación de datos de panel durante el periodo 2000-2015. Los resultados indi-can que el modelo de crecimiento regional incorpora la formación bruta de capital fijo (fbcf) y la variable proxy de las tic; adicionalmente se detecta un efecto positivo fijo para Corea del Sur.

Long Term HbA1c Prediction Using Multi-Stage CGM Data Analysis

The glycated hemoglobin (HbA1c) is regarded as an essential biomarker for diabetes management. Having an elevated HbA1c level significantly increases the risk of developing diabetes-related health complications. Accurate prediction of HbA1c can greatly improve the way diabetic patients are treated and can potentially avoid related consequences. This study devises a framework to predict HbA1c levels 2-3 months in advance by using blood glucose data collected through continuous glucose monitoring (CGM) sensors and leveraging advanced feature extraction and machine learning techniques. The CGM data may often contain missing values due to sensor issues or not wearing the sensor for some period. Thus, in the paper, a novel missing data estimation method has been proposed for a single data point, multiple data points, and entire day CGM data imputation. The CGM data have been rigorously investigated, and pertinent features were created along with a multi-stage multi-class (MSMC) classification model to predict futuristic HbA1c levels. To evaluate the developed framework, a total of 150 patients' data were sourced from Sidra Medicine, Doha, Qatar, for analysis. The proposed three-staged and five-staged MSMC models predicted HbA1c levels 2-3 months in advance and obtained overall classification accuracies of 88.65% and 83.41%, respectively.

Incubation period of coronavirus disease 2019: new implications for intervention and control

ABSTRACT The COVID-19 pandemic has been causing serious disasters to mankind. The incubation period is a key parameter for epidemic control and also an important basis for epidemic prediction, but its distribution law remains unclear. This paper analyzed the epidemiological information of 787 confirmed non-Wuhan resident cases, and systematically studied the characteristics of the incubation period of COVID-19 based on the interval-censored data estimation method. The results show that the incubation period of COVID-19 approximately conforms to the Gamma distribution with a mean value of 7.8 (95%CI:7.4–8.5) days and a median value of 7.0 (95%CI:6.7–7.3) days. The incubation period was positively correlated with age and negatively correlated with disease severity. Female cases presented a slightly higher incubation period than that of males. The proportion of infected persons who developed symptoms within 14 days was 91.6%. These results are of great significance to the prevention and control of the COVID-19 pandemic.

WITHDRAWN: Iterative SIC algorithm based detection for multiuser MIMO SC-FDMA uplink system

Abstract In this article, an iterative successive interference cancellation (SIC) method is investigated for uplink multiple input multiple output (MIMO) single carrier frequency division multiple access (SC-FDMA). The real challenge of wireless communication is reliable symbol detection in MIMO SC-FDMA system. An iterative SIC detection method is a prominent technique which acquires low complexity and near -optimal performance for MIMO systems. MIMO SC-FDMA system with renowned multiuser linear data estimation methods are minimum mean square error (MMSE) and zero forcing (ZF) which are frequently employed in single-input single- output detector scheme owing to its affordable estimation and noteworthy performance improvement. Nevertheless, this technique performs matrix inverse calculation whose matrix size related to set of subcarriers utilized in the scheme, generally, it has around thousands. In realistic scenario, matrix inversion operations turn to very high the computational complexity. Therefore, in this contribution proposes an iterative SIC algorithm which achieves near optimal performance with lower complexity. The performance of linear receiver scheme and iterative SIC algorithm is simulated for diverse antennas configuration in MIMO SC-FDMA systems. As a results illustrate that iterative SIC algorithm outperforms conventional linear detection techniques. The analysis of the new detection approach observes good performance and the reduced complexity detection in MIMO SC-FDMA systems.

Data recoverability and estimation for perception layer in semantic web of things.

Internet of Things (IoT) is the growing invention in the current development of different domains like industries, e-health, and education, etc. Semantic web of things (SWoT) is an extension of IoT that enhance the communication by behaving intelligently. SWoT comprises 7 layered architecture. The perception layer is an important layer for collecting data from devices and to communicate with its associated layer. The data loss at the perception layer is very common due to inadequate resources, unpredictable link, noise, collision, and unexpected damage. To address this problem, we propose a method based on Compressive Sensing which recovers and estimates sensory data from a low-rank structure. The contribution of this paper is three folds. Firstly, we determine the problem of data acquisition and data loss at semantic sensory nodes in SWoT. Secondly, we introduce a compressive sensing based framework for SWoT that recovers the data accurately using low-rank features. Thirdly, the data estimation method is utilized to reduce the volume of the data. Proposed Compressive Sensing based Data Recoverability and Estimation (CS-RE) method is evaluated and compared with the existing reconstruction methods. The simulation results on real sensory datasets depict that the proposed method significantly outperforms existing methods in terms of error ratio and data recoverability accuracy.

The impact of corruption on commercial banks' credit risk: Evidence from a panel quantile regression

AbstractThis study explores the impact of countrywide corruption on the credit risk of commercial banks with different levels of credit risk. It applies the quantile regression (QR) estimation method for a panel data of 191 commercial banks, from 18 MENAP countries, between the years 2011–2018. The research finding indicates that corruption significantly exacerbates the problem of bad loans of banks. Furthermore, the QR results reveal that corruption does not affect all banks at the same level. Banks in higher quantiles (i.e., higher credit risk banks) appear to be affected more than the ones in lower quantiles (i.e., lower credit risk banks).Banks with high credit risk appear to be more vulnerable to corruption than banks with low credit risk.

The role of Fiscal Institutional Context on primary balance. A case study for European country groups with cultural differences

Many countries have introduced fiscal institutions (fiscal rules and fiscal councils) to deter fiscal indiscipline, reduce macroeconomic forecasting bias and enhance the credibility of fiscal policy. In this study, we use a theoretical model in order to examine how the existence of a fiscal council can reduce a country’s debt. In addition, we examine the impact of fiscal institutions on primary balance in 2 European country groups (PIIGCS and DFGNS countries) that have cultural differences. The analysis builds on panel data estimation methods of fixed effects and random effects depending on the Hausman test results. This study finds that the fiscal institutional context (frifc) has a positive and significant effect on PB. More specifically, we find that one change in fiscal institutional context improves PB by a factor of 0.925 and 1.181 in PIIGCS countries and DFGNS countries, respectively.