Method Of Estimator Research Articles

Velocity Dispersion of the Open Cluster NGC 2571 by Radial Velocities and Proper Motions

We use a kernel density estimator method to evaluate the stellar velocity dispersion in the open cluster NGC 2571. We derive the 3D velocity dispersion using both proper motions as extracted from Gaia Data Release 3 and single-epoch radial velocities as obtained with the instrument FLAMES at ESO's Very Large Telescope. The mean-square velocity along the line of sight is found to be larger than the one in the tangential direction by a factor of 6–8. We argue that the most likely explanation for such an occurrence is the presence of a significant quantity of unresolved binary and multiple stars in the radial velocity sample. Special attention should be paid to single-line spectroscopic binaries (SB1) since in this case we observe the spectral lines of the primary component only, and therefore the derived radial velocity is not the velocity of the binary system center of mass. To investigate this scenario, we performed numerical experiments varying the fractional abundance of SB1 in the observed sample. These experiments show that the increase of the mean-square radial velocity depends on the fractional abundance of SB1 to a power in the range [0.39, 0.45]. We used the 3D velocity dispersion obtained by the dispersions in the tangential directions and the assumption that the radial velocity dispersion is the same as a tangential one to estimate the virial cluster mass and the cluster mass, taking into account the gravitational field of the Galaxy and the nonstationarity of the cluster. These estimates are 650 ± 30 M ⊙ and 310 ± 80 M ⊙, respectively, in substantial agreement with the photometric cluster mass.

Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data

This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n , T → ∞ , and the strength of the dominant units is equal to 1 when T → ∞ . For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.

Achieving sustainable development in Africa: to what extent trade facilitation matter*?

ABSTRACT Despite the extensive literature on trade facilitation and economic development, little is known about the empirical nexus of trade facilitation and sustainable development. This article analyses the role of trade facilitation in achieving sustainable development in Africa with a focus on the sustainable environment captured by carbon dioxide emissions. By employing two-stage least square and the system generalized method of moment estimators on 36 African countries over the period 2007–2019, the main results reveal a significant positive relationship between trade facilitation and sustainable environment. Moreover, our results support the inverted U-shaped relationship between GDP and CO2 emissions (Environmental Kuznets Curve theory) and between trade facilitation and CO2 emissions in Africa. Our findings suggest that African countries’ model of facilitating trade should actively build on the low-carbon development of hard and soft infrastructures.

The modified control charts for monitoring the error detection of process control under different estimators 

The reflected power function distribution (RPFD) has increasing importance in practical life due to its application in diversified fields of life. Organisations often face difficulty monitoring operations to identify and remove errors during production. That is why there is a need to introduce control charts that effectively monitor the processes, mainly when the number of errors follows RPFD and the manufacturing process is in control. The current study suggested memory-based control charts as a solution to the problem. The control charts are based on the estimation methods and play a remarkable role in enhancing the machine process reliability. The parameters of RPFD are evaluated through the percentile estimator (PE) and modified maximum likelihood estimator methods (MMLM). Further, we create memory-based control charts, i.e., hybrid exponentially weighted moving average (HEWMA) and extended exponentially weighted moving average (EEWMA), using the PE and MMLM. The findings reflect that HEWMA control charts based on PE provide a better result in estimating the defects. The implications of the study will be helpful for practitioners and policy makers from reliability engineering, management sciences, and statisticians.

Association of major depression, schizophrenia and bipolar disorder with thyroid cancer: a bidirectional two-sample mendelian randomized study

BackgroundMajor depressive disease (MDD), schizophrenia (SCZ), and bipolar disorder (BD) are common psychiatric disorders, and their relationship with thyroid cancer has been of great interest. This study aimed to investigate the potential causal effects of MDD, SCZ, BD, and thyroid cancer.MethodsWe used publicly available summary statistics from large-scale genome-wide association studies to select genetic variant loci associated with MDD, SCZ, BD, and thyroid cancer as instrumental variables (IVs), which were quality controlled and clustered. Additionally, we used three Mendelian randomization (MR) methods, inverse variance weighted (IVW), MR–Egger regression and weighted median estimator (WME) methods, to estimate the bidirectional causal relationship between psychiatric disorders and thyroid cancer. In addition, we performed heterogeneity and multivariate tests to verify the validity of the IVs.ResultsWe used two-sample bidirectional MR analysis to determine whether there was a positive causal association between MDD and thyroid cancer risk. The results of the IVW analysis (OR = 3.956 95% CI = 1.177–13.299; P = 0.026) and the WME method (OR = 5.563 95% CI = 0.998–31.008; P = 0.050) confirmed that MDD may increase the risk of thyroid cancer. Additionally, our study revealed a correlation between genetic susceptibility to SCZ and thyroid cancer (OR = 1.532 95% CI = 1.123–2.088; P = 0.007). The results of the WME method analysis based on the median estimate (OR = 1.599 95% CI = 1.014–2.521; P = 0.043) also suggested that SCZ may increase the risk of thyroid cancer. Furthermore, our study did not find a causal relationship between BD and thyroid cancer incidence. In addition, the results of reverse MR analysis showed no significant causal relationships between thyroid cancer and MDD, SCZ, or BD (P > 0.05), ruling out the possibility of reverse causality.ConclusionsThis MR method analysis provides new evidence that MDD and SCZ may be positively associated with thyroid cancer risk while also revealing a correlation between BD and thyroid cancer. These results may have important implications for public health policy and clinical practice. Future studies will help elucidate the biological mechanisms of these associations and potential confounders.

A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit

A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit

Investigating the causal role of the gut microbiota in esophageal cancer and its subtypes: a two-sample Mendelian randomization study

BackgroundThrough research on the gut microbiota (GM), increasing evidence has indicated that the GM is associated with esophageal cancer (ESCA). However, the specific cause-and-effect relationship remains unclear. In this study, Mendelian randomization (MR) analysis was applied to investigate the causal relationship between the GM and ESCA, including its subtypes.MethodsWe collected information on 211 GMs and acquired data on ESCA and its subtypes through genome-wide association studies (GWASs). The causal relationship was primarily assessed using the inverse variance weighted (IVW) method. Additionally, we applied the weighted median estimator (WME) method, MR–Egger method, weighted mode, and simple mode to provide further assistance. Subsequent to these analyses, sensitivity analysis was conducted using the MR–Egger intercept test, MR-PRESSO global test, and leave-one-out method.ResultFollowing our assessment using five methods and sensitivity analysis, we identified seven GMs with potential causal relationships with ESCA and its subtypes. At the genus level, Veillonella and Coprobacter were positively correlated with ESCA, whereas Prevotella9, Eubacterium oxidoreducens group, and Turicibacter were negatively correlated with ESCA. In the case of esophageal adenocarcinoma (EAC), Flavonifractor exhibited a positive correlation, while Actinomyces exhibited a negative correlation.ConclusionOur study revealed the potential causal relationship between GM and ESCA and its subtypes, offering novel insights for the advancement of ESCA diagnosis and treatment.

The impact of smart input subsidy program on farm productivity: Evidence from Tanzania

Policymakers have a renewed interest in input subsidy programs in sub-Saharan Africa. However, the evidence of the program's impact is mixed. This paper uses the case of Tanzania's National Agricultural Input Voucher Scheme (NAIVS) to examine its effect on farm productivity. To account for the endogeneity problem arising from the non-random targeting of beneficiaries, this study employed a staggered difference-in-differences approach with a double robust estimator method on a three-wave panel survey. This approach is relevant because the treatment variable—NAIVS participation was gradually rolled out to households over multiple periods and groups. The results do not show evidence that the NAIVS program increased farm productivity. However, the group aggregate effects suggest that the group initially treated in 2010 experienced a positive and significant impact on maize production and income. This could indicate that during the early stages of the NAIVS program, extra effort was likely invested to ensure its effectiveness. Furthermore, the heterogeneous effect of farm size reveals that the NAIVS program increased the participation of beneficiary households with large-scale farms rather than small-scale farms. This suggests that the program did not effectively target resource-poor households. Finally, this paper calls for effective targeting and careful implementation of programs to improve farmer productivity.

A Comparison Between Local Linear Regression and K- Nearest Neighbor Methods in Estimating the Nonparametric Regression Model with Simulation and Application

In this paper, the nonparametric regression model was estimated using two common estimation methods (Local Linear Regression Estimator (LLRE) and K-Nearest Neighbor Estimator (KNNE). The simulation process was conducted using the statistical programming language, R, for these methods, and they were compared using the Average Root Mean Squares Error (ARMSE) criterion, with three sample sizes (50, 100, 150), three levels of error variance (0.3, 0.7, 1) and two non-linear models. The results for the first model showed the superiority of the nearest neighbor estimator (KNNE) method in most cases, and for the second model the local linear regression estimator (LLRE) was superior at all sample sizes and for all levels of variance. On the applied side, the effect of hemoglobin (Hb) on the packed cell volume (PCV) of 150 patients with chronic kidney disease was studied. The estimation process was made using the two methods, and the comparison between them was made using the root mean square error (RMSE) criterion. It was shown through the results of the applied side that the preference was for the nearest neighbor estimator (KNNE) method.

Statistical Inference in Circular Structural Model and Fitting Circles to Noisy Data

It is well known that commonly used algorithms for circle fitting perform poorly when sampling distribution of the points is not symmetric with respect to the circle center, for example, when the points are sampled from a circle arc. To overcome this difficulty we introduce and study a parametric circular structural model. In this model the points on the circumference are assumed to be sampled according to the von Mises distribution with unknown concentration and mean direction parameters. We develop maximum likelihood and method of moments estimators of the circle center and radius, and study their statistical properties. In particular, we show that the proposed maximum likelihood estimator is asymptotically normal and efficient. We also develop a test of uniformity for the sampling distribution along the circle. Based on the derived theoretical results we propose a numerically stable circle fitting algorithm, investigate its accuracy in a simulation study, and illustrate its behavior in a real data example. Supplementary materials for this article are available online.

Wind turbine emulator using three-phase IM controlled through an adaptive reactive power estimator fed by soft-VSI topology

PurposeThe purpose of this paper aims to design a robust wind turbine emulator (WTE) based on a three-phase induction motor (3PIM).Design/methodology/approachThe 3PIM is driven by a soft voltage source inverter (VSI) controlled by a specific space vector modulation. By adjusting the appropriate vector sequence selection, the desired VSI output voltage allows a real wind turbine speed emulation in the laboratory, taking into account the wind profile, static and dynamic behaviors and parametric variations for theoretical and then experimental analysis. A Mexican hat profile and a sinusoidal profile are therefore used as the wind speed system input to highlight the electrical, mechanical and electromagnetic system response.FindingsThe simulation results, based on relative error data, show that the proposed reactive power control method effectively estimates the flux and the rotor time constant, thus ensuring an accurate trajectory tracking of the wind speed for the wind emulation application.Originality/valueThe proposed architecture achieves its results through the use of mathematical theory and WTE topology combine with an online adaptive estimator and Lyapunov stability adaptation control methods. These approaches are particularly relevant for low-cost or low-power alternative current (AC) motor drives in the field of renewable energy emulation. It has the advantage of eliminating the need for expensive and unreliable position transducers, thereby increasing the emulator drive life. A comparative analysis was also carried out to highlight the online adaptive estimator fast response time and accuracy.

Age at menarche and idiopathic pulmonary fibrosis: a two-sample mendelian randomization study

BackgroundSex difference in the incidence rate of idiopathic pulmonary fibrosis (IPF) indicates that estrogen has a certain protective effect on the disease. Nevertheless, there is a dearth of study investigating the association between factors pertaining to endogenous estrogen exposure level, such as age at menarche (AAM) in women, and IPF. Our study intended to employ Mendelian randomization (MR) method to elucidate the causal association between AAM and IPF.MethodsOur study utilized AAM as a measure of endogenous estrogen exposure and investigated its causal effect on the risk of IPF through MR. We employed the inverse variance weighted (IVW) method to assess the causal relationship between AAM and IPF risk, with supplementary analyses conducted using the weighted median estimator (WME) and MR-Egger method. Several sensitivity analyses were performed to assess the dependability of MR estimates.ResultsA total of 9 selected single nucleotide polymorphisms (SNPs) significantly associated with AAM were selected as instrumental variables. The IVW method showed that genetically later AAM was associated with an increased risk of IPF (odds ratio [OR] = 1.0014, 95%confidence interval [CI] = 1.0005–1.0023, p = 0.001). The median weighting method and the MR-Egger method obtained similar estimates, and no heterogeneity or pleiotropy was found, indicating that the results were robust.ConclusionsOur MR study suggested a causal relationship between a later onset of menarche and a heightened susceptibility to IPF.

Extended odd Weibull–Lindley distribution

A novel extension of the Lindley and Weibull distributions is proposed in this article by combining the Lindley distribution with the extended odd Weibull family, termed the extended odd Weibull–Lindley (EOWL) distribution. The statistical properties of the EOWL distribution are derived, including a linear representation for its probability density function, moments, quantiles, and moment generation functions. Three parameter estimation methods (maximum likelihood, maximum spacing product, and least squares estimations) are explored. Monte Carlo simulations are conducted to evaluate the effectiveness of the estimator methods. Finally, in order to illustrate the flexibility and validity of the proposed distribution, three real datasets are employed. The results show that the EOWL distribution demonstrates a superior fitting performance compared to other established distributions.

Source characterization of afterburning laboratory-scale jet noise with vector acoustic intensity

This paper presents an intensity-based characterization of noise sources in a highly heated laboratory-scale jet with a 3-inch exit diameter. Run conditions had nozzle pressure ratios between 2.7 and 3.5 and temperature ratios of ∼7. Measurements were performed using a microphone scanning rig containing 8 four-microphone intensity probes. Probes were separated by 1.5 nozzle diameters and the microphones were spaced 0.5 nozzle diameters apart. Each scan mapped out over 3,000 measurement locations to produce an intensity field map. Vector intensity was processed over a 200 Hz to 20 kHz bandwidth using the phase and amplitude gradient estimator (PAGE) method [D. C. Thomas, et al., J. Acoust. Soc. Am. 137, 3366–3376 (2015)]. Results show the dominant source region as a function of frequency and are compared to the acoustical holography source reconstruction of the T7-A afterburner engine condition [L. T. Mathews and K. L. Gee, AIAA J., in press (2024)]. Additionally, the intensity-derived source locations are connected to the jet’s supersonic and subsonic flow regions. [Work supported by ONR Grant No. N00014-21-1-2069].

Hazard rate estimation when the measurement error has a normal or logistic distribution

Statistical data analysis available in most scientific fields is often recorded with measurement error. The modeling of these statistical data by ignoring the measurement errors, leads to estimators of the parameters of the distributions, whose use does not achieve sufficient accuracy in the goodness of fit. In reliability criteria, one of the important issues is hazard rate function. It prompted us to investigate the hazard rate criterion in the presence of measurement error generated from the normal or logistic distribution. Now, while providing the estimator for the density function using local time polynomial estimator methods, the risk rate function is estimated according to the contamination degree of 15 or 30%. Finally, we present the numerical analysis.

A New Hybrid Algorithm Based on the Firefly Algorithm and Cuckoo Search to Estimate Survival Analysis of Truncated Inverse Gompertz Distribution

This paper introduces the Right Truncated Inverse Gompertz Distribution (RTIGD) with two parameters δ and ϑ with some of its properties as; (Survival-Function, Hazard-Function, cumulative distribution function, probability density function, ‎r-th moment, mean, variance, Moment Generating Function, Median, and Mode. In addition, we propose a new hybrid algorithm, namely the firefly Algorithm with Cuckoo Search Algorithm (FA_CSA) to estimate the parameters based on the survival functions of RTIGD. Simulation is utilized to compare the proposed algorithm with traditional methods, for example, the Maximum Likelihood Estimator and moment method and the standard algorithms Firefly Algorithm and Cuckoo Search. In most cases, the results demonstrate that the proposed algorithm (FA_CSA) provides an accurate estimation for the survival function since it has less mean squared error than the other estimation methods.

Reviewing the consequence of trade openness and financial openness on banking stability in developing countries

The global economy has fostered a dynamic environment of economic globalization, leading to amplified interconnectedness, integration, and worldwide influence in both commercial transactions and monetary activities. This occurrence emphasizes the vital role of liberalizing capital and international trade in economic discussions, particularly in emerging economies where banking-centric systems wield considerable influence. The objective of this study is to investigate the correlation between trade liberalization and financial inclusivity, specifically concerning the resilience of the banking industry in developing nations throughout the period of 2010–2020. Utilizing the dynamic data model of Arellano-Bond’s Generalized Method of Moment Estimator, this study yields a significant revelation. The interaction between trade openness and financial transparency exerts a noticeable and advantageous impact on banking stability, with each 1% increase in openness resulting in a remarkable improvement of 98.9445 in Net Interest Margin, 116.2575 in Z score, and 119.9189 in Non-Performing Loans. Consequently, this investigation confirms the presence of a diversification effect on stability while concurrently applying the concept of voltage fragility. In essence, trade openness propels the banking sector toward heightened competitiveness due to increased demand from local businesses, while financial openness fosters heightened competition within the credit market.

Groundwater–surface water exchange from temperature time series: A comparative study of heat tracer methods

The importance of the interaction between groundwater and surface water is increasingly being recognized for both understanding and managing water systems. Many efforts have been made to characterize and quantify groundwater–surface water exchange. In particular, temperature–based methods have quickly established themselves given their monetary and practical advantages. In the last 15 years, several methods for interpreting passive temperature time series measured in the streambed have been developed. Still, the benchmarking of these methods has only been carried out in specific and distinct hydrological conditions.This article aims to fill this research gap by benchmarking the performance of six commonly used methods for deriving seepage fluxes using a two-year-long temperature time series covering various meteorological and thermal conditions. This work compares three analytical methods that calculate seepage flux using the amplitude and/or phase of the temperature signals, and three numerical methods that use different schemes to inversely solve the one–dimensional heat transport equation in the streambed. The temperature measurements were made in the context of an international dispute between Chile and Bolivia over the status and use of the waters of the Silala River, Northern Chile. Flux estimations are tested against Darcy’s flux derived from measured hydraulic gradient and hydraulic conductivity.Flux estimations from the benchmarked methods ranged from −0.5 to 3.5 m/d (with positive fluxes directed downwards), whereas fluxes estimated using Darcy’s law ranged from 0.5 to 6 m/d. Results show that the amplitude method is the best–performing method. This method is best suited for estimating the direction of the fluxes, while the method using both the thermal amplitude and phase is best suited for monthly flux trends, and the combination of a Local Polynomial (LP) method and a Maximum Likelihood Estimator (MLE) method (LPMLEn) is appropriate for estimating flux in transient conditions.The use of heat as a tracer proved to be an effective tool for monitoring groundwater–surface water exchange in a river reach for two years, and yielded exchange flux estimates with lower point-scale variability than Darcy’s law.

Long-term (1993–2018) particulate polycyclic aromatic hydrocarbons (PAHs) concentration trend in the atmosphere of Seoul: Changes in major sources and health effects

This study assessed the long-term concentration trend and profiles of twelve polycyclic aromatic hydrocarbons (PAHs) occurring as airborne particulate matter in Seoul, Republic of Korea (Korea) between 1993 and 2018 to evaluate the human health effect and the effectiveness of air quality management policies. Total PAHs (∑PAHs of 12 compounds), benzo [a]pyrene (BaP), and BaP toxic equivalent (BaPTEQ) concentrations increased from 1993 to 1999 but decreased thereafter. Moreover, the BaP concentration has remained below 1 ng/m3 since 2010–2011. Benzo [b+k]fluoranthene (B [b+k]F) accounted for the largest ∑PAHs fraction (approximately 24%) for the entire period. Based on the PAH profiles, as well as the Principal Component Analysis (PCA) results and diagnostic ratio analysis, vehicular emissions and coal combustion were identified as major sources of the PAHs in Seoul. Additionally, contribution of vehicular emissions increased from 2002 to 2003 to 2018, whereas that of solid fuel combustion decreased in the same period. PAH levels increased 8-fold in winter compared to summer mainly due to increases in local and regional emissions. The implementation of policies to regulate the use of solid fuels and vehicular emissions in Korea effectively reduced Elemental Carbon (EC) and PAH emissions, as demonstrated by CO, EC, and PAHs reductions and a good correlation between EC and PAHs. Moreover, aggressive air quality management policies in China have also contributed to decreasing PAH concentration trend in Seoul. Following the incremental lifetime cancer risk (ILCR) estimations method for 8 different population groups, males in the 0–1 age group was the only group still at potential risk of PAH inhalation in 2018, while other groups have been in the acceptable risk since 2012–2013.

Assessment of mean precipitation and precipitation extremes in Iran as simulated by dynamically downscaled RegCM4

This paper aims to assess the mean precipitation and precipitation extremes over Iran as simulated by the Regional Climate Model (RegCM4). A simulation spanning 20 years (1991–2010) at a horizontal resolution of 20 Km is driven by the NCEP / NCAR reanalysis. We evaluated the model by comparing simulated precipitation with observations using Bias, Root-Mean-Square Error, and Index of Agreement metrics. We examined the extreme precipitations based on a set of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) in three categories of intensity (Rx1day and SDII), duration (CDD and CWD), and frequency (R10mm and R20mm). The linear trends are calculated using the Theil–Sen estimator method, and the statistical significance (95% confidence level) is determined by using a modified Mann-Kendall (MMK) trend test. The RegCM4 model satisfactory captured the spatial distribution of precipitation and precipitation extremes, although high bias remained in small parts of Iran, including the northwest and southeast. The northwest bias is due to spring convectional precipitation and the southeast bias could be caused by precipitation from the Asian summer monsoon system, which both of them may not be well simulated by the applied Grell convective scheme. Results indicate that the model reasonably captures Rx1day, SDII, CWD, R10mm, and R20mm Indices over Iran. In good agreement with precipitation observations, the southern coast of the Caspian Sea represents the second-highest extreme precipitation, except for SDII, which is probably due to the high frequency of rainy days in this region. The highest CDD of more than 200 days is found in the arid and semi-arid regions of the southeast. In general, precipitation decreased in most regions of Iran, especially the western, southern, and interior regions. In addition, the results reveal that heavy (R10mm) and very heavy (R20mm) precipitation events have also decreased in the same regions. Results also emphasize an increase in consecutive dry days (CDD) in most parts, especially in the southeast, which deserves more attention in future research. The decreasing trend of precipitation and the increasing trend of CDD show that Iran has become drier in the 2000 s compared to the 1990 s