Data Estimation Methods Research Articles

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill, allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Pengaruh DAR, CR, dan NPM Terhadap Harga Saham Perusahaan Telekomunikasi yang Terdaftar di BEI

Introduction: The information obtained through the study of financial statements can be used to describe a company's performance. Investors need the results of financial statement analysis as a basis for making investment decisions because stock price movements can be seen by observing them. The share price, according to theory, will fall as the value of DAR increases. Meanwhile, if the CR and NPM values increase, the stock price will increase. However, after collecting research data, it was found that there was a discrepancy in data facts according to theory. Research Methods: This study adopts a quantitative associative research methodology. In the sampling technique, a purposive sampling technique was used, and four telecommunications businesses were taken as samples. By using MS Excel 2010 and Eviews 9 software, the research approach uses panel data analysis, starting with evaluating panel data estimation methods and continuing with selecting panel data regression models, testing classical assumptions, and testing hypotheses. Results: Based on the research above, it was found that the best estimation model chosen was the Fixed Effect Model (FEM). Furthermore, using the best choice model, classical assumption testing is carried out and the result is that the classical assumption obligation has been fulfilled. Conclusion: The results of this study, partially DAR has no significant effect on stock prices, CR has a significant effect on stock prices, and NPM has a significant effect on stock prices. Meanwhile, DAR, CR and NPM simultaneously have a significant influence on the stock prices of Indonesian telecommunications companies listed on the Indonesia Stock Exchange from 2017 to 2021.

EFFECT OF R&D ON PRODUCT AND PROCESS INNOVATION IN THE TURKISH MANUFACTURING INDUSTRY

R&D activities are an indispensable driving force to increase the likelihood of occurrence of innovation and the growth rate of technology. Studies on the impact of R&D activities on sectors in Türkiye using micro data are still insufficient. To fill this gap, this study aims to analyze the effects of R&D on product and process innovation in the Turkish manufacturing industry by making sub-breakdowns of R&D variables. The research also presents policy recommendations to support R&D plans in Türkiye. To accomplish these objectives, panel data estimation methods are used. A panel data set was generated by merging four different microdata sets provided by TURKSTAT, which is classified by NACE Rev.2 and covers the years between 2009 and 2016. Probit model is used to estimate the effects of R&D expenditure, R&D intensity (as an indicator of R&D investment), R&D personnel ratio (as an indicator of know-how), and other determinants of innovation. Moreover, total R&D expenditure is separated into four breakdowns such as internal, external, external-domestic, and external-foreign. Key findings of the research show that the effects of R&D expenditure on product and process innovations vary by its sub-breakdowns. Total and internal R&D expenditure have positive effect on both product and process innovations while external and external-domestic R&D expenditure have positive effect only on product innovation, and external-foreign R&D expenditure only on process innovation. Only external, external-domestic, and external-foreign R&D intensity have positive effect on both product and process innovation.

Comparing Several Missing Data Estimation Methods in Linear Regression;Real Data Example and A Simulation Study

Analysis on incomplete could lead to biased estimation when using standard statistical procedure since it ignores the missing observations. The disadvantage of ignoring missing data is that the researcher might not have enough data to conduct an analysis. The main objective of the study is to compare the performance between listwise deletion (LD), mean substitution (MS) and multiple imputation (MI) method in estimating parameters. The performance will be measured through bias, standard error and 95% confidence interval of interested estimates for handling missing data with 10% missing observations. A complete empirical data set was used and assumed as population data. Ten percent of total observations in the population ere set as missing arbitrarily by generating random numbers from a uniform distribution, . Then, bias of parameter estimates and confidence interval of parameter estimates are calculated to compare the three methods. A Monte Carlo simulation was carried out to know the properties of missing data and investigated using simulated random numbers. Simulation of 1000 sampled data with 20, 50, and 100 observations and each sample is set to have 10% missing observations. Standard statistical analyses are run for each missing data and get the average of parameter estimates to calculate the bias and standard error of parameter estimates for every missing data method. The analysis was conducted by using SAS version 9.2. It was found that the MI method provided the smallest bias and standard error of parameter estimates and a narrower confidence interval compared to the LD and MS methods Meanwhile, the LD method gives a smaller bias of parameter estimates and standard error for small sample size of missing data. And, MS method is strongly recommended not to use for handling missing data because it will result in large bias and standard error of parameter estimates.

Smart city as a hub for talent and innovative companies: Exploring the (dis) advantages of digital technology implementation in cities

The number of smart city initiatives is growing worldwide at an incredible pace. Their ambition is to add to the competitiveness of local communities through innovation while increasing and attracting users, financial capital and talent by offering an environment suited to current needs. Digital implementation plays a key role in this evolution, facilitating cities' ability to offer and advanced urban environment tailored for its users and stakeholders. In this paper, we examine how digital technology implementation affects the attractiveness of cities in terms of intra- and international talent flows and the creation of innovative companies. Our analysis is based on a dataset of 20 Italian cities over an 11-year period. We use the GLS (Generalized Least Square) panel data estimation method, considering both direct and u-shaped effects to evidence the relationship between digital technology implementation and the attractiveness of cities for intra- and international talents, and to explore an advancing urban environment conducive to innovation and entrepreneurship. Based on empirical outcomes, this study offers valuable insights for both academics and policymakers in understanding and balancing the current interplay between digital implementation, innovative companies, and talents in contemporary cities.

Environment-growth nexus and corruption in the MENA region: Novel evidence based on method of moments quantile estimations

This study contributes to the environment-growth nexus literature by examining corruption's effect on environmental quality in 17 Middle East and North Africa (MENA) countries between 1984 and 2018. We use four different indicators of environmental degradation (ecological footprint, carbon dioxide emissions, greenhouse gas emissions, and carbon emission intensity) to gain a comprehensive view of the relationship. This study's methodological value added is the application of the method of moments quantile regression, which enables us to account for different relationships between the independent variables and environmental quality at different levels of environmental degradation. The results indicate that corruption worsens environmental quality based on three of the four indicators of environmental degradation, while the impact appears to become less severe at higher levels of environmental deterioration. The results also indicate that traditional mean-based panel data estimation methods provide an incomplete picture of the factors behind environmental problems: The explanatory variables' impacts tend to vary at different levels of environmental quality, and the impact's sign can even change when moving from the lowest to the highest environmental degradation quantiles. Overall, the results highlight the importance of curbing corruption to enable enforcement of more stringent environmental regulations.

China's marine economic efficiency: A meta-analysis

With the rapid growth of the global economy and the depletion of land-based resources, the demand for the ocean as a major driver of economic development is increasing. In China, the marine economy has been a significant “blue engine” for national economic growth; hence, it is crucial to evaluate marine economic efficiency to understand China's marine industry's input-output status and development level. Based on 559 independent observations presented in 49 empirical primary studies, we conducted a meta-analysis to quantitatively explore the differences in measurement results and identify the factors influencing marine economic efficiency. The notable results were fourfold. (1) Significant heterogeneity is evident among the findings of the primary studies. Based on the random-effect model, China's overall marine economic efficiency is 0.720, indicating that considerable potential remains for improving economic efficiency in the marine sector. (2) The data types, estimation methods, evaluation indicators, publication types, and publication years of primary studies significantly affect marine economic efficiency estimation results. (3) At the temporal level, marine economic efficiency generally demonstrates an upward trend with the increase in the sample data years. At the spatial level, the marine economic efficiency of the Circum Bohai Sea region is significantly higher than that of the Pan-Pearl River Delta region, whereas it does not significantly differ from the Yangtze River Delta region. (4) The level of marine science and technology, as well as the marine industry structure, exerts a significant positive impact on marine economic efficiency, whereas the gross ocean product negatively impacts marine economic efficiency. Accordingly, the following insights were obtained. In terms of marine economic development policies, policymakers should intensify scientific and technological innovation, balance marine resource use and marine environmental conservation, so that power a shift of the marine economy from resource-dependent growth to the orientation of better quality and benefit. In addition, priority must be given to the synergistic development of coastal regions and marine industry structure optimization and upgrading. Future studies should use long-term panel data, focus on using the parametric stochastic frontier analysis method, and assess negative environmental impacts by constructing a green evaluation index system.

Robust and Fast Normal Mollification via Consistent Neighborhood Reconstruction for Unorganized Point Clouds.

This paper introduces a robust normal estimation method for point cloud data that can handle both smooth and sharp features. Our method is based on the inclusion of neighborhood recognition into the normal mollification process in the neighborhood of the current point: First, the point cloud surfaces are assigned normals via a normal estimator of robust location (NERL), which guarantees the reliability of the smooth region normals, and then a robust feature point recognition method is proposed to identify points around sharp features accurately. Furthermore, Gaussian maps and clustering are adopted for feature points to seek a rough isotropic neighborhood for the first-stage normal mollification. In order to further deal with non-uniform sampling or various complex scenes efficiently, the second-stage normal mollification based on residual is proposed. The proposed method was experimentally validated on synthetic and real-world datasets and compared to state-of-the-art methods.

Estimation of Damage and Fracture Process by a Markov Process Inverse Analysis Model for Micro-sized CFRP

The application of carbon fiber reinforced plastics (CFRP) has been expanding as large structural members, whereas there is a growing trend to expand its use to structural members of small devices such as “drones”. From such background, this study sampled strength data from tensile tests of micro-sized unidirectionally reinforced CFRP in diameter or thickness and discussed the damage and fracture process, as well as the number nc fiber-break points up to fracture from the data. In our recent paper, the authors estimated the number of fiber-break points from previously obtained sampling data using an inverse analysis model of Markov processes. In this study, furthermore, another inverse analysis model, in which analytical solution can be obtained, was newly proposed. This model was in a good agreement with absorbing state probabilities and nc obtained from the previous model. However, it was also found that there was a data population for which the fracture probability was limited to less than one. This is because the estimation limit occurs when some of the data are somewhat far from the data population. This problem can be solved to some extent by using the maximum likelihood estimation method for complete or censored data.

KST-Mixer: kinematic spatio-temporal data mixer for colon shape estimation

ABSTRACT We propose a spatio-temporal mixing kinematic data estimation method to estimate the shape of the colon with deformations caused by colonoscope insertion. Endoscope tracking or a navigation system that navigates physicians to target positions is needed to reduce such complications as organ perforations. Although many previous methods focused to track bronchoscopes and surgical endoscopes, few number of colonoscope tracking methods were proposed because the colon largely deforms during colonoscope insertion. The deformation causes significant tracking errors. Colon deformation should be considered in the tracking process. We propose a colon shape estimation method using a Kinematic Spatio-Temporal data Mixer (KST-Mixer) that can be used during colonoscope insertions to the colon. Kinematic data of a colonoscope and the colon, including positions and directions of their centerlines, are obtained using electromagnetic and depth sensors. The proposed method separates the data into sub-groups along the spatial and temporal axes. The KST-Mixer extracts kinematic features and mix them along the axes multiple times. We evaluated colon shape estimation accuracies in phantom studies. The proposed method achieved 11.92 mm mean Euclidean distance error, the smallest of the previous methods. Statistical analysis indicated that the proposed method significantly reduced the error compared to the previous methods.

Exploring the Role of Fossil Fuels and Renewable Energy in Determining Environmental Sustainability: Evidence from OECD Countries

Global warming has become a major concern for countries around the world. In this context, developed countries have decided to reduce global emissions to achieve sustainable development. The energy mix of OECD countries consists of 80% fossil fuels and accounts for about 35% of worldwide carbon emissions. Therefore, it is important to analyze how environmental factors affect carbon emissions in OECD countries. This study uses fossil energy, renewable energy (RE), and GDP for the period 1990–2019. Unlike previous studies, we will estimate two separate models for FFE and RE. To evaluate the empirical results, advanced panel data estimation methods using the cointegration test and the CS-ARDL estimation technique are employed to examine the long-run relationship between the variables. The results of the study demonstrate that fossil fuel use and GDP increase carbon emissions both in the short and long term. However, the use of RE hurts carbon emissions and is associated with sustainable development in OECD countries. Therefore, it is assumed that although fossil fuel use degrades the environment, economic growth helps it by reducing carbon emissions. Overall, our study shows that the use of RE is essential for OECD countries to achieve their environmental sustainability goals because it reduces the share of fossil fuels in the overall energy mix. Furthermore, in order to achieve a sustainable environment, OECD countries are recommended to begin long-term planning to reduce carbon emissions.

Redshift Estimation of SDSS Photometric Data Based on AutoGluon-Mix

Redshift is an important parameter of galaxies, and the use of photometry data for redshift estimation has always been a focus in the field of astronomy. This study explores a redshift estimation method for SDSS photometric data based on the AutoGluonMix algorithm, in which the integrated learning methods, including K-nearest neighbors, random forests, XGBoosted trees, LightGBM boosted trees, CatBoost boosted trees, Extremely Randomized Trees, and neural networks, improve the accuracy of redshift estimation through comprehensive strategies. The experimental results show that AutoGluon-Mix performs extremely well compared to other algorithms for both early and late type galaxies.

Attenuation Compensation and Q Estimation of Nonstationary Data Using Semi-Supervised Learning

Traditional inverse Q filtering methods for post-stack seismic data attenuation compensation (AC) have the drawback of instability or under-compensation. Besides, the quality factor (Q) should be known as a prerequisite, which is commonly estimated using the attribute difference between the reference and observed wavelets of pre-stack VSP data. The alternating iterative AC and Q estimation method is also researched for post-stack data, while the instability or huge computation becomes a defect. In this letter, we propose a simultaneous AC and Q estimation method for nonstationary post-stack seismic data based on semi-supervised learning. Specifically, we choose the Long Short-Term Memory algorithm which is sensitive to time series and can characterize seismic signal nonlinearly with high accuracy. The proposed AC and Q estimation method employs the Q information from well-logs and the compensated high-resolution data for supervised learning, and uses nonstationary seismic data beyond wells for self-supervised learning, without the wavelet extraction procedure. The synthetic data analysis and field data applications prove the feasibility of the designed semi-supervised method in improving the vertical resolution and Q estimation. The field data impedance inversion after AC further demonstrates its effectiveness.

Labour market reforms in brazil (2017-2021): An analysis of the effects of recent flexibilization on labor market legislation

The main objective of this work is to analyze the effects of Brazilian Labor Market Reform (BLMR) in the short run and, based on identified changes in the economy in recent decades, to verify how these changes have affected the labor market. Four methods were used in a complementary way: (i) employment quality index; (ii) panel data estimation method; (iii) synthetic control method and (iv) microdata analysis. We observed that there was no considerable increase in the quality of employment and formal employment and the rate of unemployment did not decrease after the implementation of these changes. Part of this dynamic is cyclical and related to exogenous shocks, such as the COVID pandemic on the Brazilian economy. Finally, according to econometric analysis, the rate of unemployment in Brazil is more responsive to investment rates and the business environment than to unit labor cost and changes in labor legislation.

Integrating Administrative and Survey Data to Estimate WIC Eligibility and Access

Abstract The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) provides benefits to low-income, nutritionally at-risk women, infants, and children. To administer WIC, officials and program managers at the federal and state levels want to understand who is eligible for the program, who among the eligible population chooses to participate, and who is not accessing the program despite their eligibility. Novel individual-level data linkages between restricted-use WIC Administrative Records and the American Community Survey provide WIC access rates estimated at the state and county levels, as well as estimates disaggregated by the demographic and socioeconomic characteristics of individuals and their households. These estimates are developed by the Census-FNS-ERS Joint Project, a research partnership among the US Census Bureau, the US Department of Agriculture’s Food and Nutrition Service and Economic Research Service, and state WIC agencies that provide the requisite WIC administrative data to the Census Bureau. This article details and evaluates our current data linkage and estimation methods, reports results, and identifies areas for improvement and further research.

Does Uncertainty of Trade Environment Promote Green Technological Innovation? Empirical Evidence from China

In recent years, the international environment has changed rapidly, and the uncertainty of the trade environment faced by enterprises has dramatically increased. As a major participant in world trade, the uncertainty of the trade environment has significantly impacted the daily business activities and various behaviors of Chinese manufacturing enterprises. Green technology innovation has also become an important tool for enterprises to enhance their sustainable development ability and improve their ability to cope with changes in the external environment. The change in the trade environment will affect the business development of enterprises. To face the change in the trade environment, enterprises often choose to improve their technology level and increase the added value of their products. In the context of sustainable development, green technology innovation has higher technical strength and is more in line with the requirements of international development, which is strong support for enterprises to cope with the change in the trade environment. However, the current literature is scant on the association between trade uncertainty and green innovation. In this context, this study used the data of Chinese manufacturing listed companies from 2011 to 2020 to empirically analyze the relationship between trade environment uncertainty (UTE) and green technology innovation (GTI) using panel data estimation methods and further explore the related heterogeneity and its impact on sustainable development. The results show that: (1) UTE has a significant positive impact on the GTI of enterprises, and the robustness test verifies these findings. Different kinds of UTE have different effects on enterprise GTI. UTE also has a positive impact on the independent GTI of enterprises. (2) Compared with non-state-owned enterprises, the GTI of state-owned enterprises is more vulnerable to the positive impact of UTE. (3) UTE significantly and negatively impacts enterprises’ business activities and further challenges enterprises’ sustainable development. However, the enterprise’s GTI behavior, especially the substantial GTI, helps mitigate the adverse impact of UTE on the company’s sustainable development. Finally, this paper provides targeted suggestions for enterprises to better cope with UTE and improve their sustainable development capabilities.

Examining the role of sustainability and natural resources management in improving environmental quality: Evidence from Asian countries

The study examines the role of natural resources in improving environmental quality in South Asia. This research focuses primarily on the energy-environmental nexus, ignoring this important region regarding environmental quality proxies and estimation approaches. As a result, the study adopts a unique approach, examining the above mentioned effects using advanced panel data estimation methods for the period 1990–2018. The findings showed that natural resource abundance is positive, indicating its role in increasing environmental degradation in these countries. Moreover, using renewable energy negatively influences the ecological footprint, meaning that it can help reduce environmental degradation in South Asia. Furthermore, the environmental deterioration in South Asia is significantly affected by growth and population, indicating that these countries pursue economic development at the price of rising emissions that harm the environment. Overall, this research suggests that while natural resources, growing population, and economic growth both positively impact environmental quality, using renewable energy sources improves it over time. As a result, these countries need to rethink their plans and create a framework supporting long-term economic development and environmental protection.

Missing rainfall data estimation—an approach to investigate different methods: case study of Baghdad

The missing of the meteorological data in Iraq is common due to malfunction of measuring devices, security status, and human effects. The study tested 17 missing precipitation data estimation methods in Baghdad city as a case study, where, all the surrounding stations around Baghdad experienced the missing of data for various reasons, and some of the missing data are for a full year record. The methods examined in this study are based on different approaches, some of the methods are based upon the distances to the targeted station, others are upon regression factors, and there are also methods that combine several factors. There are also other types of missing data filling methods which depend on imputation and artificial intelligence. The investigation of the most accurate method to find the missing data will assist researchers and decision makers to fill the gap in their analysis in one of the most vulnerable countries in terms of drought and climate changes impacts. Results showed that Expectation Maximization (EM) method utilization has the best results with the least errors, and Multiple Linear Regression (MLR) method was ranked the second best method. In general, all of the applied methods had resulted acceptable interpolations, and it was clear that the combined methods have low significance on the results in comparison with others. All of these findings are limited to the study area meteorological and spatial conditions.

A genetic algorithm for multivariate missing data imputation

Some data mining, AI and data processing tasks might have data loss whose estimation/imputation is an important problem to be solved. Genetic algorithms are efficient and flexible global optimization methods able to deal with both multiple missing observations and multiple features such as continuous/discrete/binary data which are often found in multivariate databases unlike classical missing data estimation methods which only deal with univariate–continuous data. This paper presents a genetic algorithm to impute multiple missing observations in multivariate data which minimizes a new multi–objective (fitness) function based on the Minkowski distance of the means, variances, covariances and skewness between available/completed data. To do so, two sets of examples were tested: a continuous/discrete dataset which is compared to both the EM algorithm and auxiliary regressions, and a comparison over seven benchmark datasets.

Panel data analysis of profitability and employment growth of medium and large size industries in Ethiopia

Manufacturing industries are an asset or value added part of an economy, intimately linked with industrial and engineering design, and engaged in producing significant economic productions that can assist the national growth and development. The aim of the study was to estimate the profitability and employment growth of medium and large size industries in Ethiopia. The variables were chosen based on theoretical and experimental literature findings. In this study, an explanatory study design was implemented in carrying out this research with the prearrangement of secondary data collected from the panel data set of medium and large size manufacturing industries conducted annually by the central statistics agency over the period 2002–2011 E.C. The study employed panel data estimation methods to analyze the influence of medium and large size manufacturing industries on profitability and employment growth. Three panel data models are used: pooled ordinary least squares, fixed effect, and random effect estimation method. The Hausman test revealed the random model was the best fit for both profitability and employment growth. The diagnosticstest: normality, multicollinearity, heteroscedasticity, and autocorrelation tests were conducted on the data. The results obtained indicate that employment growth and profitability in Ethiopia are generally driven by medium and large-size manufacturing industries. The random model result shows that ownership, firm-size, advertising intensity and import intensity have a positive influence and substantial effect on profitability. But, the government has significant effect and a negative influence on profitability. The random model shows that government, advertising intensity and firm-size have significant effect and positive influence on employment growth. Nevertheless, import has significant effect and a negative influence on employment growth. Eventually, the employment growth and profitability have a positive influence and insignificant effect and the authors suggested further research in the areas by taking into account additional variables and newly emerging industries.