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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.

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.

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.

Measurement error, which occurs when some variables of interest cannot be observed exactly. It often leads to invalid conclusions and lamentable implications if ignored. Therefore, the aim of the present work is to investigate ways to handle them when the observations on both the study and auxiliary variables are spoiled with measurement errors. A general class of estimators for the estimation of population mean in presence of correlated measurement errors on study and auxiliary variables has been considered. The variations in the properties of the estimators under the influence of measurement errors has been discussed in detail. The considered class of estimators is compared with the recently proposed family of exponential-type estimators. Simulation studies are carried out to show the impact on the behavior of estimators due to variation in parameters of the measurement error model. [ABSTRACT FROM AUTHOR]

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]

To inform and optimise a cochlear implant (CI) fitting software design through an analysis of big data to define array-specific comfort (C) level profiles, frequently-used MAP parameters, and the minimum number of Neural Response Telemetry thresholds (tNRT) needed to create an accurate profile. To evaluate the software's ease of use and completion time for AutoNRT®s. MAPs analysis. Clinical study evaluating software use in creating MAPs, addressing sound-quality issues and setting patient goals. MAPs (N = 39,885); CI recipients (N = 47) and clinicians (N = 19). Distinct C-level profiles were observed for lateral-wall, contour, and slim-modiolar electrode arrays. Default settings were used for most MAP parameters (13/16) except for Pulse Width, Rate, and Maxima. Nine tNRT measurements were required for an accurate C-level profile. Measurement-time of nine tNRTs via the new algorithm was comparable to five tNRTs using the previous algorithm. Nearly all (99%) clinical tasks were completed by clinicians with the first use of the software. Most CI recipients (79.5%) rated goal-setting as valuable. Custom Sound Pro fitting software developed based on big data analysis incorporates a guided fitting workflow and expected fitting ranges. It helps to improve clinical efficiency, is easy to use and supports patient-centred care. [ABSTRACT FROM AUTHOR]

In an asymmetric population, individuals are concentrated toward one tail of the distribution. An estimator of the population mean in this asymmetric case is constructed on the basis of median ranked-set sampling, that is, the population is divided into subsets of equal size and the intersections of these sets depend on the chosen order of ranking according to a known auxiliary variable. Ranking individuals according to this auxiliary variable should approximate their ranking with respect to the unknown variable of interest. This procedure is a cost-effective way of selecting the sample when the variable of interest is unknown. To do this, the auxiliary variable must be at least weakly correlated with the variable of interest. The proposed estimator extends that constructed with extreme ranked-set sampling, whose principle is to divide the population into subsets whose intersections depend on the extreme values of the auxiliary variable. The mean square error of the estimator is expressed analytically. A simulation allows for comparing the proposed estimator with estimators based on simple random sampling and with those based on sampling sets of extreme values. A simulation shows that when the response variable is correlated with both auxiliary variables, even if these correlations are weak, around 0.5 in absolute value, then the mean square error of the proposed estimator is at least 175% lower than the mean square error of estimators based either on simple random or on extreme ranked-set samplings. A first application focuses on household incomes in the Iranian provinces of Fars and Khuzestan in 2022, first with the single gross income, which is the total income that an individual or household earns before tax as auxiliary variable and then with the two auxiliary variables of total gross household income and wages paid year-round to heads of households through the banking network. In this application, the mean square error of the proposed estimator with median ranked-set sampling is at least 60% lower than that obtained with simple random and extreme ranked-set samplings. In the application of the physical preparation score with runners' track records as an auxiliary variable concerning 160 Iranian athletes in 2022 with sample sizes of 6, 8, 10, 25, and 30, the mean square error of the proposed estimator with median ranked-set sampling is at least 50% lower than that obtained with simple random and extreme ranked-set samplings. In the third application of the COVID-19 mean mortality rate in 2022 in the USA, Iran, Turkey, and Germany, with sample sizes of 6, 8, 10, 25, and 30, estimations of the mean mortality rate are based on new cases. In each of the four countries, the mean square error of the proposed estimator under median ranked-set sampling is at least 60% lower than that obtained with simple random and extreme ranked-set samplings. [ABSTRACT FROM AUTHOR]

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 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.

This paper addresses the problem of estimating the population mean Y... of the study variable Y using information on population parameters associated with the auxiliary attribute Φ. We have suggested a class of estimators of population Y... mean using auxiliary attribute. Expressions of bias and mean squared error of the suggested class of estimators have been derived up to first order of approximation. Optimum condition is obtained at which the proposed class of estimators has minimum mean squared error. Theoretical findings are also supported through the aid of a numerical illustration. [ABSTRACT FROM AUTHOR]

In this article, we consider a new family of exponential type estimators for estimating the unknown population mean of the study variable. We propose estimators taking advantage of the auxiliary variable information under the first and second non-response cases separately. The required theoretical comparisons are obtained and the numerical studies are conducted. In conclusion, the results show that the proposed family of estimators is the most efficient estimator with respect to the estimators in literature under the obtained conditions for both cases. [ABSTRACT FROM AUTHOR]

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]

In the present paper, we have tried to discover a new calibration estimator of the population mean under stratified two-phase sampling design utilizing the information on two auxiliary variables. In order to develop the calibration estimator, we have obtained new optimum stratum weights so that the MSE/Variance of the estimator would be minimized. Moreover, we have compared the efficiency of the proposed calibration estimator with that of the calibration estimator considered under the stratified two-phase sampling scheme using single auxiliary variable. An empirical illustration along with a simulation analysis has also been presented to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a regression-type compromised imputation methods free of unknown parameters. The properties (biases and MSEs) of the proposed class of estimators are derived up to first order approximation using Taylor series approach. Also, the conditions for which the proposed estimators are more efficient than other estimators considered in the study were established. Results of numerical illustration using both real and simulated data revealed that the proposed estimators are more efficient and practicable than exiting estimators considered in the study. [ABSTRACT FROM AUTHOR]

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.

The present article proposes an estimator using the Item Sum Technique (IST) for the estimation of dynamic sensitive population mean using non-sensitive auxiliary information in the two-move successive sampling. Properties of the proposed IST estimator have been analysed. Possible allocation designs for allocating long-list and short-list samples pertaining to the IST have been elaborated. The comparison between various allocation designs has been carried out. Theoretical considerations have been integrated with numerical as well as simulation studies to show the working version of the proposed IST estimators in the two-move successive sampling. [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. [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.

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]

To overcome the missing data in sample surveys, the imputation technique plays a significant role in estimating the population mean of the study variable. Clinical data, agricultural data and business data are well-suited examples for this. Keeping this in mind, we have suggested three exponential type imputation methods and the corresponding estimators by making the optimal use of an auxiliary variable. The properties of suggested estimators have been investigated under large sample approximations. The results obtained in this study have been compared with mean estimator, ratio estimator, Kadilar and Cingi (2008) estimators, Diana and Perri (2010) estimators, Al-Omari, Bouza, and Herrera (2013) estimators, and recently Bhushan and Pandey (2016) estimators, Singh et al. (2016) estimators and Singh, Pandey, and Sharma (2020) estimators. It has been shown empirically that the suggested estimators perform effectively over all other estimators. A simulation study is also evaluated in the validation of the present work. [ABSTRACT FROM AUTHOR]

This manuscript considers the problem of estimating the population mean of the study variable y using information on two auxiliary attributes under simple random sampling without replacement (SRSWOR) scheme. We have defined a class of estimators for population mean based on two auxiliary attributes. We have studied the properties the suggested class of estimators up to first order of approximation. The regions of preferences are obtained under which the proposed class of estimators is more than some existing estimators including Haq and Shabir (2014) estimator. The optimum conditions are obtained at which the proposed class of estimators has the minimum mean squared error. This study is well supported through two numerical data sets. [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]

Abstract The present work deals with examining the effect of measurement errors on the design based statistical properties of the estimators of the population mean when the survey is encountered with some non-response. The technique of sub-sampling of non-respondents have been equipped with calibration techniques to handle the presence of non-response. Two kind of distance measures have been proposed and studied in detail to improve the calibration weights. The data on study as well as auxiliary variable has been assumed to be contaminated with correlated measurement errors. The specific properties of the estimators are derived under simple random sampling without replacement design. The proposed hybrid estimators are supported with numerical illustration incorporating simulation using specific population to show the applicability of proposed techniques practically. It is observed that the absolute relative bias of the estimators increase slightly with increase in error variance. Similarly, the precision of the estimators degrade with increase in error dispersion. Overall the proposed hybrid estimator with the chi-square type distance measure is observed to be remarkably efficient in comparison to the logarithmic type distance measure in presence of non-response and measurement errors. [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]

In this article, we suggest an improved dual to ratio cum product type exponential estimator for elevated estimation of population mean of the main variable in stratified random sampling using the known auxiliary information. The sampling properties, mainly bias and mean square error (MSE) of the introduced estimator are studied up to first order of approximation and the relative efficiency of the suggested estimator over the competing estimators are calculated theoretically as well as empirically. The goal of the study is to find the best sample allocation across strata along with minimizing the MSE of the suggested estimator. To achieve the goal, we structure the challenge as All Integer Non-linear Programming Problem (AINLPP) and solve it using genetic programming. An empirical study is done to show the efficiency of proposed estimator over other competing estimators under linear cost function. [ABSTRACT FROM AUTHOR]

In sample survey, reliable and efficient estimates are often obtained using information from auxiliary variables during estimation and designing stages. However, there are times when the auxiliary information is attribute-based. Some authors have proposed estimators using auxiliary attribute information when the population mean of auxiliary attribute is unknown. However, the estimators are ratio-based estimators which are less efficient when the bi-serial correlation between the study variable and the auxiliary attribute is negative. In this study, regression approach was used to modify estimator t d/zki to produce estimators that can be used for both negative and positive correlation. In addition, another existing estimator was also modified to produce an estimator that is independent of an unknown population parameter. The Biases and Mean Squared Errors (MSEs) of the modified estimators were determined using the Taylor series approach up to the first order of approximation. The proposed estimators' efficiency conditions over some existing estimators were established. Empirical investigations were done using stimulation study and the results revealed that proposed estimators have the lowest MSEs and the highest PREs of all the competing estimators and therefore can give better estimates of the population mean. Therefore, it can be concluded that proposed estimators have better predictive power for estimating population mean under two-phase sampling scheme. [ABSTRACT FROM AUTHOR]

Age mimicry is a well‐known phenomenon in the application of osteological age‐estimation methods. Age mimicry refers to the fact that predicting age‐at‐death from a specific trait (age indicator) based on the relation observed in a specific reference sample implies that age estimates to some degree reflect the age structure of the reference sample. In particular, the estimated population mean in a target population in which an age‐estimation method is applied is shifted towards the mean in the method‐specific reference sample. Consequently, differences in population means between different age‐estimation methods in the same target population may be due to differences in mean age of the reference samples used to develop the age‐estimation methods. We aim at quantifying the expected magnitude for such differences. Fifteen different traditional age‐estimation methods were applied to a sample of 675 adult individuals from the early medieval cemetery of Mannheim‐Seckenheim. The relation of the observed estimated population age means and the mean age in the reference samples was analyzed by linear regression. We find that up to 80% of the variation in the estimated population age means can be explained by the variation of the mean age in the reference samples. Furthermore, differences in the magnitude of 3 to 4 years in the mean age between two reference samples can imply a 1‐year difference in estimated target population age means. Because large differences in mean age between reference samples used to develop different age‐estimation methods are common, some care is needed in interpreting differences between individual age estimates or population mean age estimates in cases where different age‐estimation techniques are used. [ABSTRACT FROM AUTHOR]

Non-probability samples become increasingly popular in sampling survey with lower costs, shorter time durations and higher efficiencies. In the high-dimensional superpopulation modeling approach for non-probability samples, a model is fitted for the analysis variable from a non-probability sample, and is used to project the sample to the full population. In practice, there exist situations that the covariates in modeling process are not directly observed, but are contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable confounder. In the paper, we propose to calibrate the covariates by nonparametrically regressing the observable contaminated covariate on the confounder. We employ the SCAD-penalized least squares method to investigate the variable selection and inference problems for non-probability samples based on the calibrated covariates. A SCAD-penalized estimator for the parameter and the population mean estimator are obtained. Under some mild assumptions, we establish the "oracle property" of the proposed SCAD-penalized estimator and give the consistency properties of the proposed population mean estimator. Simulation studies are conducted to assess the finite-sample performance of the proposed method. An application to a Boston housing price study demonstrates the utility of the proposed method in practice. [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.

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.

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]

Estimation of sensitive issues is an adversely challenging task as it is highly influenced by social desirability bias leading to untrue response many times. Hence, the possible way out for these problems are randomized response technique or scrambled response technique or the item sum technique etc. The proposed work is a methodological advancement in estimation of quantitative sensitive mean in two move successive sampling by modifying the item sum technique. Detailed properties of new IST estimators have been discussed in general sampling design with applications in different sampling designs on successive moves. Theoretical considerations have been integrated with simulation studies to show the working version of modified IST on two move successive sampling. [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., 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.)

Different authors for estimating population mean have proposed several Imputation schemes. Recently, some authors have suggested generalized imputation schemes that their estimators are functions of unknown parameters of the study variable. These unknown parameters need to be estimated for the estimators to be applicable and this may require additional resources. This paper considered a class of imputation scheme that is independent of unknown parameter and the point estimator of the suggested scheme for estimating population mean was derived. The properties (bias and MSE) of an efficient estimators presented were derived up to first order approximation and also conditions for which the estimators of the proposed scheme is more efficient than other estimators of the existing schemes considered in the study were also examined. The result of the empirical study revealed that the suggested estimators are more efficient than the existing ones considered in the study. [ABSTRACT FROM AUTHOR]

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]

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 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]

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]