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Abstract The existence of measurement errors cannot be avoided in practice. It is a prominent fact that the existence of measurement errors diminishes conventional properties of the estimators. A modified correlated measurement errors model has been proposed. Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566–5593. 10.1080/03610918.2016.1165845, 2017) correlated measurement errors model is a particular member of the suggested modified model. In this article, we have tackled the estimation of population mean utilizing auxiliary information under modified correlated measurement errors model. We have developed ratio and product estimators and studied their properties in case of simple random sampling without replacement (SRSWOR) up to first order of approximation. It has been illustrated that suggested ratio and product estimators are more efficient than the conventional unbiased estimator as well as Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566–5593. 10.1080/03610918.2016.1165845, 2017) ratio and product estimators under very realistic situations. An empirical study has also been performed to demonstrate the merits of the recommended estimators over other estimators.

In this article, we offer simple random sampling (SRS) based efficient class of estimators of population mean Y¯ utilizing additional information. The expression of the mean square error of the proposed class of estimators is deduced up to first degree approximation. The efficiency conditions are established which are enhanced numerically utilizing a simulation study consummated over symmetrical and asymmetrical populations. Real data sets are also utilized to exemplify the suggested estimators. The numerical findings are appeared rather acceptable demonstrating better advancement over the ordinary estimators.

In this article, we offer simple random sampling (SRS) based efficient class of estimators of population mean Y ¯ utilizing additional information. The expression of the mean square error of the proposed class of estimators is deduced up to first degree approximation. The efficiency conditions are established which are enhanced numerically utilizing a simulation study consummated over symmetrical and asymmetrical populations. Real data sets are also utilized to exemplify the suggested estimators. The numerical findings are appeared rather acceptable demonstrating better advancement over the ordinary estimators. [ABSTRACT FROM AUTHOR]

Environmental monitoring and assessment aim to gather data economically, without bias, using efficient and cost-effective sampling methods. One such traditional method is Ranked Set Sampling (RSS), often employed to achieve observational economy. This article introduces an innovative two-stage sampling approach for ranked set sampling (RSS) to get a more precise estimate of the population mean. Modified Median Quartile Double Ranked Set Sampling (MMQDRSS) highlights the ranked base technique's potential as a cost-effective sampling method. To evaluate the performance of the proposed estimator by using real-life data and conducting a simulation study to compare the relative efficiency of the proposed estimator with some existing methods.

Increasing demand in surveys focused on quantitative characteristics (population mean among others) of controversial issues like corruption, tax evasion, drugs consumption of sensitive variables like spending on drugs or illegal sources of income which lead to lively research, are of randomized response techniques for quantitative variables. Therefore, we propose complex multicriteria evaluation methodology for randomized response techniques for population mean. Based on extensive review in the literature, following ranges of criteria were proposed: statistical properties of estimator, implementation and parameter choice, respondent burden and credibility and confidentiality protection of respondents’ data. Finally, we evaluate in this setting standard techniques using scramble variables and recently proposed techniques of dichotomous question.

This paper presents some efficient classes of estimators of population mean on current occasion in the presence of random non-response under a two-phase successive sampling set-up. The suggested classes of estimators are proposed for simple random sampling under various situations of non-response. The properties of proposed estimators have been discussed up to first order of approximation. The efficiency of the presented estimators has been contrasted with the estimators for the complete response scenarios. Two real and two artificially generated data sets are used. The efficacy of the proposed classes of estimators over the existing estimators is checked theoretically and empirically. The numerical comparison supports the proposed estimators. [ABSTRACT FROM AUTHOR]

Abstract This paper presents some efficient classes of estimators of population mean on current occasion in the presence of random non-response under a two-phase successive sampling set-up. The suggested classes of estimators are proposed for simple random sampling under various situations of non-response. The properties of proposed estimators have been discussed up to first order of approximation. The efficiency of the presented estimators has been contrasted with the estimators for the complete response scenarios. Two real and two artificially generated data sets are used. The efficacy of the proposed classes of estimators over the existing estimators is checked theoretically and empirically. The numerical comparison supports the proposed estimators.

Increasing demand in surveys focused on quantitative characteristics (population mean among others) of controversial issues like corruption, tax evasion, drugs consumption of sensitive variables like spending on drugs or illegal sources of income which lead to lively research, are of randomized response techniques for quantitative variables. Therefore, we propose complex multicriteria evaluation methodology for randomized response techniques for population mean. Based on extensive review in the literature, following ranges of criteria were proposed: statistical properties of estimator, implementation and parameter choice, respondent burden and credibility and confidentiality protection of respondents' data. Finally, we evaluate in this setting standard techniques using scramble variables and recently proposed techniques of dichotomous question. [ABSTRACT FROM AUTHOR]

The performance of a population mean estimator can be improved by transformation techniques using a subset of the population data. An improved family of estimators for population mean has been proposed under double sampling using a transformed auxiliary variable. The biases and mean square errors of the proposed family of estimators up to the first order of approximation have been investigated. Simulation studies and an application to fine particulate matter in Chiang Rai, Thailand, are used to study the efficiency of the proposed estimators. The results from an application to air pollution in Chiang Rai showed that the proposed estimators gave smaller biases, by at least a half of the existing ones, and gave at least four times less mean square error than the existing ones.

Antoch, Mola and Vozár (2022) proposed recently new randomized response technique for population mean or total of a quantitative variable. The aim of the paper is to solve the open problem to derive unbiased variance estimator of these procedures. In their proposal, unlike other randomized response techniques for population mean or total the randomized response is not a linear function of a sensitive variable. However, standard techniques to derive variance estimators in this setting are based on this assumption. That is why an interviewer needs also to know values pseudorandom numbers (i.e., results of individual randomization experiments). Respondents might perceive this relaxation of privacy protection negative. The performance of the approximate two-sided confidence intervals of distributions with different shape including their coverage is assessed by a simulation study for simple random sampling without replacement.

Exploration of solar irradiance can greatly assist in understanding how renewable energy can be better harnessed. It helps in establishing the solar irradiance climate in a particular region for effective and efficient harvesting of solar energy. Understanding the climate provides planners, designers and investors in the solar power generation sector with critical information. However, a detailed exploration of these climatic characteristics has not yet been studied for the Southern African data. Very little exploration is being done through the use of measures of centrality only. These descriptive statistics may be misleading. As a result, we overcome limitations in the currently used deterministic models through the application of distributional modelling through quantile functions. Deterministic and stochastic elements in the data were combined and analysed simultaneously when fitting quantile distributional function models. The fitted models were then used to find population means as explorative parameters that consist of both deterministic and stochastic properties of the data. The application of QFs has been shown to be a practical tool and gives more information than approaches that focus separately on either measures of central tendency or empirical distributions. Seasonal effects were detected in the data from the whole region and can be attributed to the cyclical behaviour exhibited. Daily maximum solar irradiation is taking place within two hours of midday and monthly accumulates in summer months. Windhoek is receiving the best daily total mean, while the maximum monthly accumulated total mean is taking place in Durban. Developing separate solar irradiation models for summer and winter is highly recommended. Though robust and rigorous, quantile distributional function modelling enables exploration and understanding of all components of the behaviour of the data being studied. Therefore, a starting base for understanding Southern Africa's solar climate was developed in this study. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has attracted the attention of many scholars due to its outstanding performance. However, PSO has the defects of easily falling into local optimum and low precision. To alleviate these defects, this paper presents a hierarchical particle swarm optimization based on mean value (mHPSO). Firstly, based on the mean value of the population, the whole population is divided automatically into low or high level. Secondly, according to the characteristics of particles at different levels, different speed and position updating strategies are designed, respectively, which are used to balance the global and local search capabilities and improve the accuracy of the solution. Thirdly, a random neighbor selection mechanism is embedded into the update process of the highlevel particle to keep the population diversity. Finally, compared with other PSO variants, mHPSO has better performance and faster convergence speed in solving some benchmark test functions with different types. Moreover, by combining mHPSO with Otsu, mPSO also shows good performance in image segmentation. [ABSTRACT FROM AUTHOR]

Using the information in x, we consider a new estimator that uses an exponential function to estimate the unknown population mean of y in the case of non-responding units. These cases are divided into two categories as Case I and Case II. In Case I, non-response units are only available on y, whereas in Case II, non-response units are available on both x and y. The proposed estimators are derived from both scenarios, accordingly. The necessary comparisons are made theoretically and numerical study on the subject of education is carried out. We conclude that in both non-response schemes, the proposed estimators can be chosen in theory and also in applications, such as the education data.

In this paper, two generalized class such as ratio to exponential and the difference type of estimators have been proposed for estimating the finite population mean by utilizing information on higher order moments of an auxiliary variable in the two traditional situations of non-response. The expressions of biases and mean squared errors of the estimators are derived up to first order of approximation. The efficiency comparisons of the proposed methods over other contemporary estimators are carried out on real data sets. A simulation study is also performed to support the present study. The performance of the proposed methods are better than the others existing methods. Results are interpreted and suitable recommendations are made.

The performance of a population mean estimator can be improved by transformation techniques using a subset of the population data. An improved family of estimators for population mean has been proposed under double sampling using a transformed auxiliary variable. The biases and mean square errors of the proposed family of estimators up to the first order of approximation have been investigated. Simulation studies and an application to fine particulate matter in Chiang Rai, Thailand, are used to study the efficiency of the proposed estimators. The results from an application to air pollution in Chiang Rai showed that the proposed estimators gave smaller biases, by at least a half of the existing ones, and gave at least four times less mean square error than the existing ones. [ABSTRACT FROM AUTHOR]

Antoch, Mola and Vozár (2022) proposed recently new randomized response technique for population mean or total of a quantitative variable. The aim of the paper is to solve the open problem to derive unbiased variance estimator of these procedures. In their proposal, unlike other randomized response techniques for population mean or total the randomized response is not a linear function of a sensitive variable. However, standard techniques to derive variance estimators in this setting are based on this assumption. That is why an interviewer needs also to know values pseudorandom numbers (i.e., results of individual randomization experiments). Respondents might perceive this relaxation of privacy protection negative. The performance of the approximate two-sided confidence intervals of distributions with different shape including their coverage is assessed by a simulation study for simple random sampling without replacement. [ABSTRACT FROM AUTHOR]

In this paper, two generalized class such as ratio to exponential and the difference type of estimators have been proposed for estimating the finite population mean by utilizing information on higher order moments of an auxiliary variable in the two traditional situations of non-response. The expressions of biases and mean squared errors of the estimators are derived up to first order of approximation. The efficiency comparisons of the proposed methods over other contemporary estimators are carried out on real data sets. A simulation study is also performed to support the present study. The performance of the proposed methods are better than the others existing methods. Results are interpreted and suitable recommendations are made. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of estimating the population mean for non-response using simple random sampling. A new family of estimators is proposed for estimating the population mean with auxiliary information on the sample mean and the rank of the auxiliary variable. Bias and mean square errors of existing and proposed estimators are obtained using the first order of measurement. Theoretical comparisons are made of the performance of the proposed and existing estimators. We show that the proposed family of estimators is more efficient than existing estimators in the literature under the given constraints using these theoretical comparisons.

The treatment of incomplete data is an important step in statistical data analysis of most survey datasets. Missing values creates a boisterous situation for the survey researchers in producing the precise estimate of the desired population parameters. To handle these situations, imputation methods play a significant role in filling incomplete response values when it is necessary to use information on complete sampled units and not to discard the data with missingness. Keeping this in mind, our motive is to propose various improved exponential type imputation methods and the corresponding resultant estimators by using ancillary information. The properties (biases and mean square errors) of developed estimators have been examined. It has been shown that the estimators of population mean under similar circumstances due to Prasad [1-3] and some other estimators are special case of our suggested class of estimators. Results are obtained by using simulation studies and it shows the desired performance over others.

In this article, we proposed a novel dual to ratio cum product type exponential estimator in stratified random sampling. The bias and mean square error up to the first degree of approximation of the proposed estimator have been obtained. The proposed estimator has been compared with the unbiased estimator in stratified random sampling, combined ratio and product estimators, dual to combined ratio, and product estimators. Our results showed a great improvement in terms of relative efficiency. Also, the results are supported by empirical studies. [ABSTRACT FROM AUTHOR]

Exploration of solar irradiance can greatly assist in understanding how renewable energy can be better harnessed. It helps in establishing the solar irradiance climate in a particular region for effective and efficient harvesting of solar energy. Understanding the climate provides planners, designers and investors in the solar power generation sector with critical information. However, a detailed exploration of these climatic characteristics has not yet been studied for the Southern African data. Very little exploration is being done through the use of measures of centrality only. These descriptive statistics may be misleading. As a result, we overcome limitations in the currently used deterministic models through the application of distributional modelling through quantile functions. Deterministic and stochastic elements in the data were combined and analysed simultaneously when fitting quantile distributional function models. The fitted models were then used to find population means as explorative parameters that consist of both deterministic and stochastic properties of the data. The application of QFs has been shown to be a practical tool and gives more information than approaches that focus separately on either measures of central tendency or empirical distributions. Seasonal effects were detected in the data from the whole region and can be attributed to the cyclical behaviour exhibited. Daily maximum solar irradiation is taking place within two hours of midday and monthly accumulates in summer months. Windhoek is receiving the best daily total mean, while the maximum monthly accumulated total mean is taking place in Durban. Developing separate solar irradiation models for summer and winter is highly recommended. Though robust and rigorous, quantile distributional function modelling enables exploration and understanding of all components of the behaviour of the data being studied. Therefore, a starting base for understanding Southern Africa’s solar climate was developed in this study.

In the present paper, stratified mixture ranked set sampling (SMIRSS) for estimation of the population mean and median is suggested. The SMIRSS provides unbiased estimator of population mean for symmetric distribution. To test the performance of estimators under suggested scheme a simulation study is conducted. The simulation results indicate that the SMIRSS is more efficient than the stratified extreme ranked set sampling (SERSS) and stratified simple random sampling (SSRS) designs. Moreover, in small sample the suggested scheme is more efficient than stratified ranked set sampling (SRSS) for some distributions. [ABSTRACT FROM AUTHOR]

This paper aims to propose information hiding with the variant like steganography and watermarking in proposed 10.6 kbps Conjugate Structure-Algebraic Code Excited Linear Prediction (CS-ACELP) speech codec. Proposed work makes use of Dither Modulation-Quantization Index Modulation technique to incorporate steganography or watermarking for information hiding in the excitation codebook code vector of proposed 10.6 kbps CS-ACELP based speech codec. Codebook partition and label assignment approach is explored in proposed coder in order to create room of 10 bits per frame for steganographic data transmission. Joint source coding and data hiding approach is adopted for steganographic or watermarked data transmission. Performance of the proposed approach is evaluated with different objective and subjective parameters in terms of tables and graphs. Information hiding capacity is demonstrated with different parameters like the watermark to signal ratio, hiding capacity and embedding capacity in terms of percentage. Moreover, the results of subjective and objective parameters of the proposed algorithm are analysed by computing population mean of 99% confidence interval to prove the consistency of it. [ABSTRACT FROM AUTHOR]

Controlling and improving quality is a major business strategy for various organizations. A company gains an advantage over its competitors by maintaining a high level of quality for products or services and pleasing its customers or clients. Control charts are a tool used in Statistical Process Control for the purpose of reducing variability in a process as well as estimating certain parameters. In the current manuscript, auxiliary information has been utilized to propose an estimator for process mean using hybrid exponentially moving average statistic. The proposed memory-type estimator utilizes prior information to provide better estimates from data which is time-scale based than conventional estimators which were designed for data collected at a single point of time. The properties of the estimator have been studied under Simple Random Sampling as well as Stratified Sampling structure. Simulation study has been conducted to illustrate its efficiency over contemporary estimators. Application to real data on datasets “Machine”, “Automobile” and “Auto MPG” is introduced to illustrate the method.

This paper proposed an estimator of population mean in the presence of non-response on study variable. Expression for bias and MSE of this ratio-cum-product type of generalized estimator has been derived up to first order approximation. Bias and MSE of several sub-cases of the proposed estimator has been given in the presence of non-response. Efficiency comparison has been made with Hansen-Hurwitz estimator of population mean in presence of non-response. Non-response has also applied on both study and auxiliary variable. Empirical study has conducted to check the efficiency of the estimator. Comparison with other sub-cases is made on the basis of PRE value with respect to the Hansen-Hurwitz estimator of population mean. The study has showed that the new suggested estimator has the potential of wider applicability over these existing estimators. [ABSTRACT FROM AUTHOR]

Using the information in x, we consider a new estimator that uses an exponential function to estimate the unknown population mean of y in the case of non-responding units. These cases are divided into two categories as Case I and Case II. In Case I, non-response units are only available on y, whereas in Case II, non-response units are available on both x and y. The proposed estimators are derived from both scenarios, accordingly. The necessary comparisons are made theoretically and numerical study on the subject of education is carried out. We conclude that in both non-response schemes, the proposed estimators can be chosen in theory and also in applications, such as the education data. [ABSTRACT FROM AUTHOR]

The treatment of incomplete data is an important step in statistical data analysis of most survey datasets. Missing values creates a boisterous situation for the survey researchers in producing the precise estimate of the desired population parameters. To handle these situations, imputation methods play a significant role in filling incomplete response values when it is necessary to use information on complete sampled units and not to discard the data with missingness. Keeping this in mind, our motive is to propose various improved exponential type imputation methods and the corresponding resultant estimators by using ancillary information. The properties (biases and mean square errors) of developed estimators have been examined. It has been shown that the estimators of population mean under similar circumstances due to Prasad [1-3] and some other estimators are special case of our suggested class of estimators. Results are obtained by using simulation studies and it shows the desired performance over others. [ABSTRACT FROM AUTHOR]

Surveys related to sensitive issues are accompanied with social desirability response bias which flaw the validity of analysis. This problem became serious when sensitive issues are estimated on successive occasions. The scrambled response technique is an alternative solution as it preserve respondents anonymity. Therefore, the present article endeavours to propose an improved class of estimators for estimating sensitive population mean at current occasion using an innocuous variable in two occasion successive sampling. Detailed properties of the estimators are analysed. Optimum allocation to fresh and matched samples are obtained. Many existing estimators in successive sampling have been modified to work for sensitive population mean estimation under scrambled response technique. The proposed estimators has been compared with recent modified estimators. Theoretical considerations are integrated with empirical and simulation studies to ascertain the efficiency gain derived from the proposed improved class of estimators.

Environmental monitoring and assessment aim to gather data economically, without bias, using efficient and cost-effective sampling methods. One such traditional method is Ranked Set Sampling (RSS), often employed to achieve observational economy. This article introduces an innovative two-stage sampling approach for ranked set sampling (RSS) to get a more precise estimate of the population mean. Modified Median Quartile Double Ranked Set Sampling (MMQDRSS) highlights the ranked base technique's potential as a cost-effective sampling method. To evaluate the performance of the proposed estimator by using real-life data and conducting a simulation study to compare the relative efficiency of the proposed estimator with some existing methods., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

We propose families of estimators for the population mean using an exponential function in case of non-response. This situation is examined under two cases, Case I and II. The bias, MSE and minimum MSE are separately obtained for both cases. We compare the proposed estimators theoretically with the main estimators from the literature, such as Hansen and Hurwitz (1946), ratio, regression and exponential estimators. The conditions for which the proposed estimators are most efficient are obtained. Moreover, different empirical studies are conducted to support the theoretical results for both cases.

We propose families of estimators for the population mean using an exponential function in case of non-response. This situation is examined under two cases, Case I and II. The bias, MSE and minimum MSE are separately obtained for both cases. We compare the proposed estimators theoretically with the main estimators from the literature, such as Hansen and Hurwitz (1946), ratio, regression and exponential estimators. The conditions for which the proposed estimators are most efficient are obtained. Moreover, different empirical studies are conducted to support the theoretical results for both cases. [ABSTRACT FROM AUTHOR]

Introduction According to the classic sampling theory, errors that are mainly considered in the estimations are sampling errors. However, most non-sampling errors are more effective than sampling errors in properties of estimators. This has been confirmed by researchers over the past two decades, especially in relation to non-response errors that are one of the most fundamental non-immolation errors. More done researches, had studied non-sampling errors individually, however, in practice, two or more non-sampling errors occur together. In this paper, are considered two types of non-sampling errors in the stage of estimation of population mean: measurement error and non-response error. Has been proposed a new estimator for estimating the mean population in the presence of measurement and non-response errors. It has been shown theoretically and experimentally that the proposed estimator is more efficient than the Hansen and Hurwitz's unbiased estimator and other existing estimators. Material and methods Since we can have populations for which the correlation between the studied variable and the auxiliary variable can be positive or negative simultaneously, for the positive correlation, the ratio estimator, and for the negative correlation, we need an product estimator. Therefore, we need an estimator that can express both conditions. For such populations, it is suggested that the estimator combines the ratio exponentiol estimator and the product exponentiol estimator using the probability balance method. Results and discussion The effect of measurement and non-response errors on the population mean is simultaneously examined and has been proposed a class of new estimators for the estimation of the mean of the variable population studied, when the measurement and non-response errors occur in both variable studied, and the proposal . The proposed estimator is compared with some of the existing estimators both theoretically and empirically, and it has been shown that in either case the proposed estimator is more efficient than the other estimators. Conclusion The following conclusions were drawn from this research. In each of the six communities, the proposed estimator is better than the other estimators. proposed method is easy to implement and it is a powerful mathematical tool to obtain the new population mean estimators with little additional works../files/site1/files/41/8Extended_Abstract.pdf

In this paper, we have considered the problem of estimating the population mean using one or two auxiliary variables in the presence of non response under two phase sampling when study variable itself is qualitative in nature. Bias and mean squared error (MSE) expressions of the classes of estimators are derived up to the first order of approximation. The proposed estimators have been compared with the usual estimator. An empirical study has been carried out to show that the proposed class of estimators using auxiliary variables are of great importance in increasing the efficiency of the survey. [ABSTRACT FROM AUTHOR]