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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]

The present study concerns the issue of estimating the population mean and presents novel and improved regressed exponential estimators using different parameters of an auxiliary character based on sub-sampling non-respondents. The bias and mean square error (MSE) of the proposed estimators for the most pragmatic simple random sampling without replacement (SRSWOR) scheme have been derived up to the first order of approximation (i.e. the expression containing errors up to the power of two so that the expectation comes only in terms of the mean, variance and covariance). The optimum value of the MSE of the estimators is found, along with the necessary conditions for optimising the MSE. The effectiveness of the suggested estimators, outperforming the existing ones in terms of their MSE, has been studied theoretically, while the empirical as well as the simulation studies have confirmed these findings. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

In the presented paper, authors propose a new estimator by combining two already exist ratio estimators and estimate the population mean in simple random sampling. Authors also determine the mean square error (MSE) of this estimator. In the last of this paper, they show that the presented estimator is more efficient than the existing ratio estimators theoretically and numerically. [ABSTRACT FROM AUTHOR]

In the present paper, we have proposed some improved estimators of the population mean utilizing the information on two auxiliary variables with the use of two-phase sampling scheme under non-response. We have discussed the situation in which study and first auxiliary variables suffer from the non-response while the second auxiliary variable is free from the non-response. The expressions for the bias and mean square error of the proposed estimators have been derived up to the first order of approximation. We have compared the efficiency of the proposed estimators with that of the usual mean estimator and some well known existing estimators of the population mean. The theoretical results have also been illustrated through some empirical data. [ABSTRACT FROM AUTHOR]

The present article endeavours to develop partial optional randomized - response technique (PORT) to deal with sensitive issues in presence of non-response in successive sampling. Calibration techniques have been embedded with PORT to estimate sensitive population mean at current move in two move successive sampling in presence of non-response. Optimum calibration weights are computed at each move with the aid of constraints based on auxiliary information. Detailed properties of the proposed estimators have been discussed. Possible cases in which non-response may creep at two moves has been explored. The proposed technique has been compared with the modified existing technique. Simulation results indicate that the proposed technique is more efficient than existing, modified one. Suitable recommendations are forwarded. [ABSTRACT FROM AUTHOR]

This manuscript considers a dual to product and ratio estimator for estimating the finite population mean of study variable on applying a simple transformation to the auxiliary variable by using its average values in the population that is generally available in practice. The mean square error (MSE) of the proposed estimator has been obtained to the first degree of approximation. The optimum values and range of suitably chosen scalar, under which the proposed estimator perform better, have been determined. A method to lower the MSE of the proposed estimator relative to that of the MSE of the linear regression estimator is developed for small sample sizes. Theoretical and empirical studies have been done to demonstrate the superiority of the proposed estimator over the other estimators. [ABSTRACT FROM AUTHOR]

Under vague and imprecise environment, data analysis methods are usually integrated with the fuzzy set theory. Intuitionistic methods sets are the extension of ordinary fuzzy sets whose aim is to incorporate the hesitancy of experts into their model. In this paper, novel intuitionistic fuzzy parametric approaches to statistical hypotheses testing for the population mean based on intuitionistic fuzzy data and intuitionistic fuzzy hypotheses are presented. First, bootstrap method is applied and then intuitionistic fuzzy test statistics is obtained. Applications of one sided and two sided hypotheses are provided. [ABSTRACT FROM AUTHOR]

The estimation of finite population mean at current occasion in two occasion successive sampling in presence of non-response is investigated using tuned jackknife estimators. Based on the availability of auxiliary information at population level (Info U) and sample level (Info s) and using tuned jackknife technique, estimators have been proposed. Estimator of variance of proposed estimators have also been discussed. Different cases of occurance of non-response have been explored. The estimators are mutually compared. The properties of these estimators are studied via simulation study using natural population. [ABSTRACT FROM AUTHOR]

The problem of estimation of sensitive population mean has been investigated using the item sum technique (IST) with calibration estimators in two occasion successive sampling. Generic sampling design have been assumed at each occasion to define calibration estimators. Properties of the proposed estimators has been discussed including asymptotic variance. Different possible allocation designs for allocating long list and short list samples pertaining to IST has been elaborated. Simulation study using a natural population has also been added to substantiate the theoretical results. [ABSTRACT FROM AUTHOR]

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]

This paper introduces an innovative and optimal randomized response model by making use of correlated scrambling variables for estimating the population mean of a sensitive variable. The resultant estimators are found to be unbiased. Variance expressions of the proposed estimators have also been derived. Analytical as well as empirical evidences are provided in favour of the proposed estimators relative to their competitors. A possible extension to a general method is also suggested. [ABSTRACT FROM AUTHOR]

In this paper we have considered the problem of estimating the population mean using auxiliary information in sample surveys. A class of dual to ratio estimators has been defined. Exact expressions for bias and mean squared error of the suggested class of dual to ratio estimators have been obtained. In particular, properties of some members of the proposed class of dual to ratio estimators have been discussed. It has been shown that the proposed class of estimators is more efficient than the sample mean, ratio estimator, dual to ratio estimator and some members of the suggested class of estimators in some realistic conditions. Some numerical illustrations are given in support of the present study. [ABSTRACT FROM AUTHOR]

In this paper, a sampling scheme named 'mixture ranked set sampling' (MIRSS) for estimation of the population mean and median is suggested. The MIRSS is applicable when the ordinary RSS cannot be fully conducted in all cycles of the experiment. We show that when the underlying distribution is symmetric, MIRSS provides unbiased estimator of population mean. Moreover, unbiased estimator of population variances is derived. A simulation study is conducted to evaluate the performance of the estimators under suggested scheme for both perfect and imperfect ranking. Our simulation results showed that the proposed scheme is more efficient than the extreme ranked set sampling (ERSS) and simple random sampling (SRS). In addition, the MIRSS is more efficient than ordinary RSS design when ranking is not perfect. The suggested scheme is also illustrated using real data set. [ABSTRACT FROM AUTHOR]

In this article, we have compared the performance of ratio-type estimators in some stratified ranked set sampling methods. These sampling methods are stratified random sampling, stratified ranked set sampling, strati-fied double ranked set sampling and stratified median ranked set sampling. In these methods, the ratio type estimators using auxiliary variable information such as coe¢ cient of variation and kurtosis are examined. We have used a real data set to see the performance of estimators. We use the data concerning body mass index (BMI) as a study variable and the age and the weight as auxiliary variables for 800 people in Turkey in 2014. We stratified the data set using gender. A simulation study is carried out to see performance of the proposed ratio type estimators in these stratified ranked set sampling de- signs. The performances of these estimators are compared in terms of mean squared error (MSE) and percent relative e¢ ciency (PRE). The importance of this study is to compare these stratified sampling designs with those in the sampling literature by performing a detailed simulation using a real data set. [ABSTRACT FROM AUTHOR]

The problem of the estimation of quantitative sensitive variable using the item sum technique (IST) on successive occasions has been discussed. IST difference, IST regression, and IST general class of estimators have been proposed to estimate quantitative sensitive variable at the current occasion in two occasion successive sampling. The proposed new estimators have been elaborated under Trappmann et al. (Journal of Survey Statistics and Methodology, 2, 58-77, 2014) as well as Perri et al. (Biometrical Journal, 60, 155-173, 2018) allocation designs to allocate long list and short list samples of IST. The properties of all proposed estimators have been derived including optimum replacement policy. The proposed estimators have been mutually compared under the above mentioned allocation designs. The comparison has also been conducted with a direct method. Numerical applications through empirical as well as simplistic simulation has been used to show how the illustrated IST on successive occasions may venture in practical situations. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimation of population mean of a sensitive characteristics using non-sensitive auxiliary variable at current move in two move successive sampling. The proposed estimator is studied under five different scrambled response models. Various estimators have been elaborated to be the member of the proposed class of estimators. The properties of the proposed estimators have been analysed. Many estimators belonging to the proposed class have been explored under five scrambled response models. In order to identify the scrambled model effect, the proposed composite class of estimators is compared to the direct methods. Respondents privacy protection have also been elaborated under different models. Theoretical results are supplemented with numerical demonstrations using real data. Simulation has been carried out to show the applicability of proposed estimators and hence suitable recommendations are forwarded. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient stratified randomized response model based on Chang et al.'s (2004) model. We have obtained the variance of the proposed estimator of πs, the proportion of the respondents in the population belonging to a sensitive group, under proportional and Neyman allocations. It is shown that the estimator based on the proposed model is more efficient than the Chang et al.'s (2004) estimator under both proportional as well as Neyman allocations, Hong et al.'s (1994) estimator and Kim and Warde's (2004) estimator. Numerical illustration and pictorial representation are given in support of the present study. [ABSTRACT FROM AUTHOR]

This paper advocates the problem of estimating the population mean Ȳ of the study variable y using information on two auxiliary variables (x, z). We have suggested a class of estimators for population mean Ȳ of the study variable y based on the knowledge of the population means (X, Z ) of auxiliary variables (x, z), respectively. The bias and mean squared error of the proposed class estimators are obtained under large sample approximation. We have obtained the optimum conditions at which the suggested class of estimators is more efficient than the usual unbiased estimator y , st ratio and product estimators and the one recently proposed by Tailor et al. (2012). An empirical study is carried out in the favour of present study. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new technique for estimating the population mean called as Multistage Partitioned Ranked Set sampling (MPRSS) using the notion of Partitioned Ranked set Sampling (PRSS). It may be served as a generalisation of PRSS. The estimator formed under the newly suggested technique is found to be more efficient than its competing estimators. An empirical study has been carried out to support the theoretical findings. [ABSTRACT FROM AUTHOR]

This article proposes an alternative to usual ratio estimator of population mean in post-stratified sampling procedure and its properties are analyzed. Both theoretical and empirical findings are encouraging and support the soundness of the proposed procedure for mean estimation over an alternative to ratio estimator in simple random sampling without replacement suggested by Srivenkataramana and Tracy (1980), usual combined ratio estimators suggested by Ige and Tripathi (1989), and usual unbiased estimator in post-stratified sampling scheme. Both theoretical and empirical findings are encouraging and support the soundness of the present study. At the end, a simulation study has been carried out to verify the superiority of the proposed estimator. [ABSTRACT FROM PUBLISHER]

This paper discusses the problem of estimation of population mean in case of post-stratification. Improved ratio- and product-type exponential estimators of finite population mean are suggested with their case of post-stratification. Bias and mean-squared error of the suggested estimators are obtained up to the first degree of approximation. Suggested estimators have been compared with unbiased estimator, ratio estimator, and product estimator in case of post-stratification. An empirical study has been carried out to demonstrate the performance of the suggested estimator. [ABSTRACT FROM PUBLISHER]

The occurrence of nonresponse is very much plebeian in surveys, which troubles the analysis, and hence, an inappropriate inference is left out. To counterbalance the sour effects of the incompleteness, fresh imputation techniques have been proposed with the aid of multi-auxiliary variates for the estimation of population mean on successive waves. Properties of the proposed estimators have been elaborated, and they have been compared with the work of Priyanka et al. (2015). Detailed simulation study is carried out to substantiate the empirical and theoretical results. Several possible cases have been addressed in which nonresponse can occur. [ABSTRACT FROM AUTHOR]

This paper proposes modification in the transmission of excitation codevector and its non-zero pulse sign magnitude using 'codebook partition and label assignment' approach, which in turn reduces the number of bits required to transmit it through the communication channel in legacy CS-ACELP 8 kbps speech codec. The proposed approach uses the excitation codebook structure of forward mode standard G.729E 11.8 kbps with two non-zero pulses per track which avoids the use of two algebraic codebook structure for forward mode as well as for backward mode of G.729E with least significant pulse replacement approach for finding optimized excitation codevector. Proposed modification in legacy 8 kbps CS-ACELP (80 bits/10 ms) speech codec actuates the bit rate of 10.6 kbps (106 bits/10 ms) with a better objective and subjective analysis in stark contrast with legacy 8 kbps CS-ACELP speech coder and also avoids the switching of codebook modes of standard 11.8 kbps (G.729E) CS-ACELP speech coder. This paper also aims to propose the reduction in the number of searches in the final codevector of excitation structure by considering initial codevector as a final codevector which improves the quality of the speech compared to the output speech quality of legacy G.729 CS-ACELP working at 8 kbps. Both legacy CS-ACELP 8 kbps speech codec and proposed CS-ACELP 10.6 kbps are implemented in MATLAB. Subjective and objective analysis are carried out on a proposed CS-ACELP 10.6 kbps speech codec in order to evaluate its performance and the results obtained are then cross- compared with the results of legacy CS-ACELP (8 kbps) using set of tables and graphs. It is evident from obtained results that both PESQ and MOS scores are quite comparable for each set of wave files even though bitrates are reduced. Consistency and efficiency of proposed algorithm is assured by calculating the population mean of 95% confidence interval based on obtained objective and subjective parameter results. [ABSTRACT FROM AUTHOR]

In the present paper, we have considered the problem of estimation of population mean in the presence of non-response under two-stage sampling. Two different models of non-response with deterministic response mechanism have been discussed in the paper. The estimators under two non-response models have been developed by using Hansen and Hurwitz (1946) technique. The expressions for the variances and estimates of variance of these estimators have been derived. The optimum values of sample sizes have been obtained by considering a suitable cost function for a fixed variance. A limited simulation study has been carried out to examine the magnitude of percent relative loss (% RL) in standard error due to non-response. An empirical study with the real populations has also been carried out to assess the % RL in standard error due to non-response. [ABSTRACT FROM AUTHOR]

In this article, we have proposed six classes of estimators for population mean using auxiliary variable in stratified population with known population mean in the presence of non response on the study variable. The properties of the proposed class of estimators have been studied for fixed sample size. An empirical study has been given in the support of the proposed classes of the estimator. [ABSTRACT FROM AUTHOR]

In this paper, it is shown that an estimator of the population mean using known coefficient of variation and a preliminary test of significance for the variance σ²y = σ²0 is found to be better than the usual regression estimator. [ABSTRACT FROM AUTHOR]

We consider the problem of estimation of a finite population mean (or proportion) related to a sensitive character under a randomized response model when independent responses are obtained from each sampled individual as many times as he/she is selected in the sample and prove the admissibility of a sampling strategy in a class of comparable linear unbiased strategies. We prove that the admissible strategy is also optimal in this class under a super-population model. [ABSTRACT FROM AUTHOR]