Estimation Of Mean Research Articles

The Relationship between Commercial, Technological, Research (Multidimensional) Complexity Indices and Financial Development

The source of economic growth is the supply-demand balance. Demand refers to consumption and supply refers to production. Producing quality products plays an important role on the production side of this balance. The country, which has a wide range of qualified products called sophisticated products, continues to grow with financial development. The concept of economic complexity refers to product diversity. This concept has taken its place in the literature by being expanded into commercial, technological, and research complexity indices as the content of the multidimensional complexity index. In this study, the relationship between multidimensional complexity indices and financial development indicators of E7 (Emerging) countries (Brazil, China, India, Indonesia, Mexico, Russia, Turkey) is analyzed with Gengenbach, Urbain, and Westerlund Cointegration Test and Mean Group Estimator applied in case of cointegration. It has been determined that these variables are cointegrated, and this relationship between the commercial and technological complexity index and financial development is significant. As a result of the analysis with the mean group estimator, it was concluded that the technological complexity index has a greater impact on financial development than the commercial and research complexity indices.

Technology Oriented Struggle Against Climate Change in Transportation Sector: An Empirical Investigation

The transportation sector is one of the most important sectors in which greenhouse gas emissions (GHG) are the highest, thus causing the global warming problem to rise. One of the most effective and international solutions to this problem is considered to be a technology-oriented struggle, and the development of green technologies is encouraged by global authorities. The study aims to investigate the success of the technology-oriented struggle against global warming in the transport sector. In analyses, data on transportation-related greenhouse gas emissions, the number of patents (transport-related climate change mitigation technologies), trade openness, and GDP per capita of 12 OECD countries years between 1999-2017 were used. To identify the long-run and short-run relationship among variables, the Cross-Sectional Autoregressive Distributed Lags Estimator (CSARDL) and also the Mean Group (MG), Augmented Mean Group (AMG), and Common Correlated Effects Mean Group Estimators (CCE) were applied. According to the estimators' findings, no evidence was found that the number of patents and trade openness affected greenhouse gas emissions, but it was determined that GDP positively affected greenhouse gas emissions. As a result, it can be said that the technology-oriented struggle against climate change in the transportation sector alone isn't sufficient to reduce transportation-related GHG emissions.

Weighted Ranked Set Sampling for Skewed Distributions

Ranked set sampling (RSS) is a useful technique for improving the estimator of a population mean when the sampling units in a study can be more easily ranked than the actual measurement. RSS performs better than simple random sampling (SRS) when the mean of units corresponding to each rank is used. The performance of RSS can be increased further by assigning weights to the ranked observations. In this paper, we propose weighted RSS procedures to estimate the population mean of positively skewed distributions. It is shown that the gains in the relative precisions of the population mean for chosen distributions are uniformly higher than those based on RSS. The gains in relative precisions are substantially high. Further, the relative precisions of our estimator are slightly higher than the ones based on Neyman’s optimal allocation model for small sample sizes. Moreover, it is shown that the performance of the proposed estimator increases as the skewness increases by using the example of the lognormal family of distributions.

Solving the missing at random problem in semi‐supervised learning: An inverse probability weighting method

We propose an estimator for the population mean under the semi‐supervised learning setting with the Missing at Random (MAR) assumption. This setting assumes that the probability of observing , denoted by , depends on the total sample size and satisfies . To efficiently estimate , we introduce an adaptive estimator based on inverse probability weighting and cross‐fitting. Theoretical analysis reveals that our proposed estimator is consistent and efficient, with a convergence rate of , slower than the typical rate, due to the diminishing proportion of labelled data as the sample size increases in the semi‐supervised setting. We also prove the consistency of inverse probability weighting (IPW)–Nadaraya–Watson density function estimators. Extensive simulations and an application to the Los Angeles homeless data validate the effectiveness of our approach.

A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network

The proportion of wind energy in global energy structure is growing rapidly, promoting the development of wind power forecasting (WPF) technologies to solve the uncertainty and intermittence of wind power generation. However, the nonlinear and stochastic features of wind power time series restrain the accuracy of multi-step prediction performance. A multi-step WPF (MS-WPF) approach based on a time series bi-level empirical mode decomposition (BLEMD) method and BiLSTM neural network is proposed in this paper to improve the WPF accuracy of regional wind power generators. Since the uncertainty is always generated through coupled factors from both wind and weather-to-power conversion, the linearity feature is first introduced as an aspect apart from the frequency in the proposed approach to decompose the wind power time sequence data. The proposed BLEMD introduces Pearson product-moment correlation coefficient to evaluate the linearity of time series and a linearity-based decomposition algorithm is designed accordingly. To further enhance the precision and release computation burdens, a DL-based prediction strategy, including a BiLSTM network, a CNN-BiLSTM network, and a mean weight estimation method are implemented to predict the components separately. The proposed method only relies on local data, greatly reducing the data acquisition and computation cost. The precision of the proposed MS-WPF is verified by a 2.5 kW wind turbine with horizons from 5 s to 30 s, a 1.5 MW wind turbine with horizons from 10 min to 1 h, and a 51 MW wind farm with horizons from 1 h to 6 h. The comparative experimental results with other cutting-edge methods indicated that the proposed MS-WPF has superior prediction accuracy and stable performance for multi-step prediction.

Evaluating the relationship between income inequality, renewable energy and energy poverty in the V4 countries

The European Union has prioritized addressing energy poverty, as outlined in the “Clean Energy for all Europeans Package”. The recent international context dominated by the COVID-19 pandemic and Russia war in Ukraine, have exacerbated this issue because of the substantial energy prices growth. Since eradicating energy poverty necessitates long-term policy implementation, the primary objective of this study is to evaluate the impact of income inequality and other economic and social factors (output per capita, renewable energy use) on energy poverty as measured by three indicators (total utility bill arrears, utility bill arrears for households with dependent children, population unable to maintain adequate). The findings based on mean group estimators for the V4 (Poland, Hungary, Czech Republic, Slovakia) countries from 2005 to 2022 suggest that income inequality, proxied by the Gini index, exacerbates energy poverty. However, sustainable economic growth has the potential to alleviate energy poverty. Policy recommendations include reducing income inequality, reforming renewable energy policies, to encourage the development of renewable energy sector and green industries.

Principal Component Analysis of Morphological Traits in Udaipuri Goats in Uttarakhand, India

Aims: To study the principal component analysis (PCA) of morphological traits in Udaipuri goats. Place and Duration of Study: Pauri, Garhwal, Uttarakhand, India. Methodology: Eight traits were measured in 407 adult (doe) and (buck) goats over a period of 2021-2022. The traits considered in the study were Horn Length (HL), Face length (FL), Ear Length (EL), Height at Withers (HW), Body Length (BL), Chest Girth (CG), Tail Length (TL) and Body Weight (BW). The data were analyzed for the estimation of mean, coefficient of variation (CV %), phenotypic correlation and PCA by using the software SPSS version 24. Results: The descriptive statistics including the mean and CV % were estimated which revealed the lowest average and the highest average for horn length and chest girth, respectively. The horn length and ear length showed the highest and lowest CV %, respectively, and the phenotypic correlations were significant (P<0.01) for all the characters included in the study. Principal component analysis (PCA) with varimax rotation method was applied and extracted a total variation of 66.01%. In all adult males and females, HL, HW, BL and CG were loaded as first component while FH, EL, TL and BW were loaded as second component. Conclusion: Therefore, these findings indicated that PCA could serve as a valuable tool in breeding programmes, allowing for a significant reduction in the number of traits while still effectively capturing morphometric trends in Udaipuri goats.

Credibility Theory for Variance Premium Principle

The method of credibility theory plays a critical role in various research areas of actuarial science. Among others, the hypothetical mean and process variance are two quantities that convey crucial information to insurance companies when determining premiums for the insureds. The classical credibility model charges premiums by applying a linear combination of the mean of past claims and the population mean, and the corresponding estimator is proved to be the best estimator of the hypothetical mean under the mean squared loss criterion. Enlightened by the prestigious variance premium principle, we propose a credibility approach to estimate the linear combination of hypothetical mean and process variance under the quadratic loss function. Our proposed estimator consists of the linear form of observations and their quadratic terms, as well as some quantities representing population information. Meanwhile, a spin-off result is found and utilized to compare with the classical credibility model and the q-credibility model. The nonparametric estimators of structural quantities are also provided for ease of practical usage. Several numerical illustrations are carried out to demonstrate the performance of the estimator. A real dataset from a Swiss insurance company is also analyzed for the practical application of our results, where a data-driven procedure is proposed for determining the safety loading.

Conditional bias adjusted estimator of treatment effect in 2-in-1 adaptive design

ABSTRACT For implementation of adaptive design, the adjustment of bias in treatment effect estimation becomes an increasingly important topic in recent years. While adaptive design literature traditionally focuses on the control of type I error rate and the adjustment of overall unconditional bias, the research on adjusting conditional bias has been limited. This paper proposes a conditional bias adjustment estimator of treatment effect under the context of 2-in-1 adaptive design and aims to provide a comprehensive investigation on their statistical properties including bias, mean squared error and coverage probability of confidence intervals. It demonstrated that conditional bias adjusted estimators greatly reduce the conditional bias and have similarly negligible unconditional bias compared with mean and median (unconditional) unbiased estimators. In addition, the test statistics is constructed based on the conditional bias adjustment estimators and compared with the naive unadjusted test.

Ratio-type estimator of the population mean in stratified sampling based on the calibration approach

An improved ratio-type calibrated estimator was developed using the logarithmic mean in the calibration constraint for the stratified random sampling scheme. The proposed estimator was extended in the case of stratified double sampling and compared with the estimators given by Tracy et al. (2003) together its ratio-type estimators, as well as Nidhi et al. (2017) and Khare et al. (2022). A simulation study was also carried out on both a real and artificial dataset in order to evaluate the performance of the proposed estimator compared to the existing estimators.

Estimation of finite population mean of a sensitive variable using three-stage optional RRT in the presence of non-response and measurement errors

The purpose of this study is to present a generalized class of estimators using the three-stage optional randomized response technique (RRT) in the presence of non-response and measurement errors on a sensitive study variable. The proposed estimator makes use of dual auxiliary information. The expression for the bias and mean square error of the proposed estimator are derived using Taylor series expansion. The proposed estimator's applicability is proven using real data sets. A numerical study is used to compare efficiency of the proposed estimator with adapted estimators of the finite population mean. The suggested estimator performs better than adapted ordinary, ratio, and exponential ratio-type estimators in the presence of both non-response and measurement errors. The efficiency of the proposed estimator of population mean declines as the inverse sampling rate, non-response rate, and sensitivity level of the survey question increase.

Efficient Classes of Robust Ratio Type Estimators of Mean and Variance in Adaptive Cluster Sampling

This paper proposes two classes of robust ratio type estimators of finite population mean and two classes of robust ratio type estimators of finite population variance using a single auxiliary variable under the adaptive cluster sampling design. Seven robust ratio type estimators have been developed from each class. The generalized expressions of bias and mean square error for each class have been derived up to the first order of approximation. The exact expressions of bias and MSE for all the developed estimators have been presented. Using simulation studies conducted in R programming, the high efficiency of all the developed estimators over similar existing estimators in adaptive cluster sampling has been demonstrated. . KEYWORDS :Adaptive cluster sampling, Classes of robust ratio type estimators, Hidden clustered data, Estimation of mean, Estimation of variance.

Efficiency study of a robust regression-type estimator for population mean under different ranked set sampling methods with outlier handling

Efficiency study of a robust regression-type estimator for population mean under different ranked set sampling methods with outlier handling

Ameliorating the diagnostic power of combined shewhart-memory-type control chart strategies for mean

A great deal of research in statistical process control (SPC) involves the integration of different ideas to achieve the optimal detection of anomalies in the location parameter of a process. Likewise, the prime rationale of the combined (Shewhart-Memory-type) strategies is to promptly detect the shift of any size: small, moderate or large. An effort, along the same lines, is made to detect shifts in the mean of a normally distributed process. The usual difference estimator of the process mean instead of the sample mean is employed in the Mixed EWMA-Crosier CUSUM Combined Shewhart Mixed EWMA-CUSUM and Combined Shewhart Mixed EWMA-Crosier CUSUM charts to make their upgraded versions. This enhances the sensitivity of these charts against any shift size. The performance of the charts is appraised and compared with some notable charts of the same ilk, with reference to the average run length (ARL), relative average run length, extra quadratic loss and performance comparison index. An illustrative example is also provided.

Estimation of Population Mean in an Infinite Population Using Samples by a Systematic Manner

Systematic sampling method has been used in a finite population for estimation of the population characteristic. In this article, we are trying to extend this method when the population size is countably infinite. In finite population, a unit has equal probability of being chosen in most of the cases except PPS. However, this cannot be so in infinite population. In that case,we approximate infinite population by large finite population and then use the traditional finite population method. It is true that the original population have some distribution, but without knowing that we are using systematic sampling method and based on that we are estimating population mean. We want to see the effectiveness of this systematic manner if the population has some specified distribution. Here, based on Poisson assumption, we provide some approximation methods and see performances in each case. However, this is to be done for some important distributions also. Here, the problem has been viewed in three ways and out of that the first method performs well, where the Poisson probabilities has been assigned to all possible samples rather than any truncation of the infinite population by finite population. . KEYWORDS :Systematic sampling, Risk function, Poisson distribution, Truncation, Approximation methods.

An efficient ratio estimator of population means under double sampling technique using information on auxiliary attribute

This paper provides the estimation of population mean under double sampling, using supplementary information in the form of an attribute (that is, a proportion of the population possessing an attribute highly correlated with the study variable). A generalized efficient family of ratio estimator of the population mean is suggested and expressions for the bias and the mean square error of the estimator, as well as the minimum value are obtained. The proposed estimator has an improvement over some existing estimators when compared numerically using a natural population data and therefore provides a better substitute for the purpose of estimation.

Mean Estimators for Sensitive Variables Using Scrambled Response Models

The most popular methods for sensitive study are regression estimation methods that use standard regression coefficients. When it comes to survey researchers, mean estimation is an important issue. The vast majority of population mean estimation methods that can be found in sampling theory are meant only to be utilized with non-sensitive data-sets. When the variable that is important is sensitive, such as drug usage, illegal income, abortion, exam cheating, the amount of income tax due, employee rule-breaking, etc., these estimation methods cannot operate efficiently. In this article, we propose a novel method for computing the population mean using exponential technique of robust-type regression estimators for scrambled response model (SRM) under simple random sampling (SRS). The mean square error (MSE) equation is generated using a first-order approximation and examined with existing estimating techniques in order to evaluate the efficacy of the new approach. Additionally, the proposed estimator's percentage relative efficiency (PRE) is determined compared to other estimators. The effectiveness of the proposed method is demonstrated using real data sets. According to the results, the suggested estimator performs better than other estimators in the literature.

A new family of robust quantile-regression-based mean estimators using Sarndal approach

In presence of extreme-values or outliers, the ratio-type mean estimators are prominently developed by using the robust coefficients of regression and covariance matrices. In this article, we consider quantile regression coefficients while estimating the finite population mean. Incorporating Sarndal concept, we offer quantile-regression-based mean estimators under a simple random sample design. We calculate the mean squared error (MSE) for the suggested estimators and discover that they perform better than the existing estimators. Moreover, numerical representations and a simulation study support our discoveries in theory.

On duration effects in non-life insurance pricing

The paper discusses duration effects on the consistency of mean parameter and dispersion parameter estimators in exponential dispersion families (EDFs) that are the standard models used for non-life insurance pricing. Focus is on the standard generalised linear model assumptions where both the mean and variance, conditional on duration, are linear functions in terms of duration. We derive simple convergence results that highlight consequences when the linear conditional moment assumptions are not satisfied. These results illustrate that: (i) the resulting mean estimators always have a relevant asymptotic interpretation in terms of the duration adjusted actuarially fair premium—a premium that only agrees with the standard actuarial premium using a duration equal to one, given that the expected value is linear in the duration; (ii) deviance based estimators of the dispersion parameter in an EDF should be avoided in favour of Pearson estimators; (iii) unless the linear moment assumptions are satisfied, consistency of dispersion and plug-in variance estimators can not be guaranteed and may result in spurious over-dispersion. The results provide explicit conditions on the underlying data generating process that will lead to spurious over-dispersion that can be used for model checking. This is illustrated based on real insurance data, where it is concluded that the linear moment assumptions are violated, which results in non-negligible spurious over-dispersion.

Performance of different modeling techniques in testing the impact of environmental variables on eel landing in Ichkeul Lake, a RAMSAR Wetland and UNESCO biosphere reserve

Advanced modeling techniques, including Random Forest (RF) and Cubist model (CB), were used to assess the relationship between environmental factors and European eels (Anguilla anguilla) abundance and to provide insights into the lake's ecological status while considering climate change and anthropogenic influences. A comprehensive dataset, attained through extensive environmental and biological monitoring for the period 2010–2020, was employed. The performance of the models is carried out using key metrics including the root mean square error (RMSE), coefficient of determination (R²), and mean estimation error (MAE). In addition, a sensitivity analysis was conducted to ascertain the relative significance of the thirteen input variables in shaping the predictions of the models. The precision of the CB and RF models in predicting eel landings surpassed that of Multiple Regression. In the training dataset, the CB model achieved R2=0.55, RMSE=7.68 tons, and MSE=6.20 tons, and the RF model achieved R2=0.56, RMSE=7.20 tons, and MSE=5.56 tons. High accuracy was maintained on the testing dataset, with CB achieving R2=0.73, RMSE=5.13 tons, and MSE=5.89 tons, and RF achieving R2=0.73, RMSE=5.81 tons, and MSE=4.67 tons. The scatter plot between predicted and measured eel landings indicated that the RF model tends to overestimate lower values and underestimate higher values of eel landings, while the CB model gave better performance in this context. Further, the carried sensitivity analysis using the CB model unequivocally identified three pivotal factors – water level, salinity, and turbidity level – as the most influential determinants governing the landing of eels in this ecosystem. Thus, the CB model is considered to be more promising for interpreting the relationship between environmental parameters and eel landings, which could be used by managers for an effective lake management strategy.