Estimation of population mean on the most recent occasion under non response in h-occasion successive sampling using several auxiliary variables

In this article, general estimation procedures of population mean on the most recent occasion in the presence of non response on all occasions have been deduced in h-occasion successive sampling. In the estimation procedures, regression type estimators have been suggested with the aid of stable and mutually independent several auxiliary variables which are readily available only on the most recent occasion. To check the efficiency of the proposed estimators, they have been compared with similar estimator in absence of non response. Properties of the suggested estimation procedures have been studied and their empirical studies are carried out to validate the theoretical results. Suitable recommendations have been made.     

Effectual variance estimation strategy in two-occasion successive sampling in presence of random non response

Present investigation deals with the problem of random non response in estimation of population variance on current occasion in two-occasion successive sampling. To reduce the negative impact of random non response on both occasions, regression type imputation method has been suggested. Using auxiliary information, efficient estimation strategies have been developed for estimation of population variance on the current occasion. Estimator for the population variance is also derived as a special case when random non response occurs only on the current occasion. Empirical studies are carried out to show the dominance of suggested estimators over sample variance and exponential type estimators. Results are interpreted.     

Some effective combinations of available information under non response in two-occasion successive sampling

The present work suggests some effective linear combinations of exponential estimators to estimate the current population mean in two occasion successive sampling in presence of non response. Using the subsampling technique of non respondents, estimation procedures have been developed by utilizing information on an auxiliary variable, which is readily available on both occasions. Properties of the proposed estimation procedures have been examined and related optimum replacement strategies are suggested. Empirical studies are carried out to justify the preposition of estimators. Suitable recommendations have been made.     

Estimation of population mean in presence of random non response in two-stage cluster sampling

The present investigation addresses the problem of estimating a finite population mean in two-phase cluster sampling in presence of random non response situations. Utilizing information on an auxiliary variable, regression type estimators has been proposed. Effective imputation techniques have been suggested to deal with the random non response situations. The properties of the proposed estimation strategies have been studied for different cases of random non response situations in practical surveys. The superiority of the suggested methodology over the natural sample mean estimator of population mean has been established through empirical studies carried over the data sets of natural population and artificially generated population.     

An Improved Procedure of Estimating the Finite Population Mean Using Auxiliary Information in the Presence of Random Non Response

This article deals with the problem of estimation of the finite population mean using auxiliary information in the presence of random non response. Three different situations where random non response occurs either in study variate, or in auxiliary variate, or in both the variates, have been discussed. The asymptotically optimum estimators (AOEs) for each strategy are also identified. Expressions of biases and mean squared errors of the proposed estimators have been derived up to the first degree of approximation. Proposed estimators have been compared with the usual unbiased estimator, ratio estimator, and product estimator in the presence of random non response. Empirical studies are also carried out to show the performance of the proposed estimators over other estimators.     

Use of multiauxiliary variables to cope with non response in estimation of current population mean in two-occasion successive sampling

ABSTRACT

The present work utilizes the information on multiauxiliary variables to neutralize the negative effect of non response in estimation of current population mean in two-occasion successive sampling. Generalized exponential type estimators have been proposed for the situation when non response occurs at both occasion or at first occasion or at current (second) occasion in two-occasion successive sampling. Properties of the proposed estimators have studied and empirical studies are carried out to justify the preposition of estimators. Suitable recommendations have been made.     

Estimation under non response when it occurs on both occasions in two-occasion successive sampling

ABSTRACT

The present paper is an attempt to analyze the effect of non response at both occasions in two-occasion successive sampling. Using the sub-sampling of non respondent technique, exponential type estimators have been proposed to estimate the current population mean. Properties of the proposed estimators are examined and respective optimum replacement strategies are suggested. Empirical studies are carried out to evaluate the performances of the proposed estimators. Results are interpreted and suitable recommendations have been made.     

Estimation of population mean in successive sampling under super-population model in presence of random non response situations

The present work is an attempt to estimate the population mean on the current occasion in two-occasion successive (rotation) sampling in presence of random non response situations. The estimation strategy has been constructed under a super-population model design approach with the help of imputation technique. The estimators proposed on the current occasion cover the cases of occurrences random non responses on either of the occasions. Detail behaviors of the proposed class of estimators have been studied and its performance has been examined with the sample mean estimator. The results are demonstrated through empirical studies which establish the effectiveness of the proposed class of estimators. Suitable recommendations have been put forward to the survey statisticians for its practical application.     

Grey Predictive Process Control Charts

The present article intends to develop some imputation methods to reduce the impact of non response at both the occasions in two-occasion successive (rotation) sampling. Utilizing the auxiliary information, which is only available at the current occasion, estimators have been proposed for estimating the population mean at the current occasion. Estimators for the current occasion are also derived as a particular case when there is non response either on the first occasion or second occasion. Behaviors of the proposed estimators are studied and their respective optimum replacement policies are also discussed. To study the effectiveness of the suggested imputation methods, performances of the proposed estimators are compared in two different situations, with and without non response. The results obtained are demonstrated with the help of empirical studies.     

Some efficient classes of estimators of population mean in two-phase successive sampling under random non response

In this paper, an attempt has been made to present some efficient classes of estimators of population mean on current occasion in two-occasion successive sampling under random non response in two-phase setup. Effectiveness of the proposed classes of estimators has been studied under the assumptions that sampling units follow a distribution under the random non response. To check the performances, the proposed classes of estimators are compared with an estimator under the similar situation under the complete response. Results are demonstrated through empirical studies which show the reliable nature of the proposed classes of estimators. Suitable recommendations have been made to the survey practitioners.     

Some Imputation Methods to Minimize the Effect of Non Response in Two-Occasion Rotation Patterns

Among modern strategies applied to cope with non response, a major problem faced by survey statisticians, imputation is one of the most common. Imputation is the filling up method of incomplete data for adapting the standard analytic model in statistics. The purpose of the present work is to study the use of imputation methods in dealing with non response which occur at both occasions in two-occasion successive (rotation) sampling. Chain-type regressions in ratio estimators have been proposed for estimating the population mean at current occasion. Expressions for optimum estimator and its mean square error have been derived. To study the effectiveness of the imputation methods, performances of the proposed estimators are compared in two different situations: with and without non response. Behavior of the proposed estimators is demonstrated through empirical studies.     

Improved two phase sampling exponential ratio and product type estimators for population mean of study character in the presence of non response

Improved two phase sampling exponential ratio and product type estimators for population mean using known coefficient of variation of study character in the presence of non response have been proposed and their properties are studied under large sample approximation. The proposed estimators are compared with the other existing estimators by using the MSE criterion and the conditions under which the proposed estimators perform better are obtained. An empirical study is also given to judge the performance of the proposed estimators. At the end, simulation studies have been carried out to verify the superiority to the proposed estimators.     

Improved Ratio- and Product-Type Exponential Estimators for Population Mean in Case of Post-Stratification

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.     

Estimation of Population Mean Using Known Coefficient of Variation of the Study Character in the Presence of Non Response

Using the known coefficient of variation of the study character, generalized and regression-type estimators for the population mean using two phase sampling in the presence of non response were proposed and their properties have been studied. The conditions under which the proposed estimators are more efficient than the relevant estimators have been obtained. The empirical studies were given in the support of the problems in the case of positive and negative correlation between the study and the auxiliary characters which show the increase in the efficiency of the proposed estimators using known coefficient of variation of the study character with respect to the relevant estimators.     

Local Linear Regression for Non Grid Spatiotemporal Models with Autoregressive Errors

In various environmental studies, spatiotemporal correlated data are involved, so there has been increasing demand of proposing spatiotemporal estimation methods that capture spatiotemporal correlation so as to improve the accuracy of estimation. In this article, we construct estimators for non grid spatiotemporal models with autoregressive errors. It is proved that the estimators are asymptotic normality. Simulation results also show the estimators perform well.     

A general procedure for estimating the mean using double sampling for stratification and multi-auxiliary information

This paper defines a general procedure for estimating the population mean of the study variate based on double sampling for stratification in presence of multi-auxiliary information. Classes of combined and separate estimators have been suggested and their properties are studied under large sample approximation. A class of unstratified double sampling estimators is also proposed with its properties. Asymptotic optimum estimators in the classes are identified with their approximate variance formulae. Further the proposed classes of estimators are compared with the corresponding class of estimators based on un-stratified double sampling. All findings are encouraging and support the soundness of the proposed procedure for mean estimation.     

Linear Increments with Non‐monotone Missing Data and Measurement Error

Linear increments (LI) are used to analyse repeated outcome data with missing values. Previously, two LI methods have been proposed, one allowing non‐monotone missingness but not independent measurement error and one allowing independent measurement error but only monotone missingness. In both, it was suggested that the expected increment could depend on current outcome. We show that LI can allow non‐monotone missingness and either independent measurement error of unknown variance or dependence of expected increment on current outcome but not both. A popular alternative to LI is a multivariate normal model ignoring the missingness pattern. This gives consistent estimation when data are normally distributed and missing at random (MAR). We clarify the relation between MAR and the assumptions of LI and show that for continuous outcomes multivariate normal estimators are also consistent under (non‐MAR and non‐normal) assumptions not much stronger than those of LI. Moreover, when missingness is non‐monotone, they are typically more efficient.     

Estimating the Concentration of Propagules of a Plant Pathogen from Soil Dilution Plate Counts when the Response is Non-linear

The estimation of micro-organism concentrations from dilution plate data is discussed for situations where expected counts are not proportional to the amount of sample per plate. Aspects of design and analysis are investigated in relation to an alternative non-linear model in which the concentration is given by the slope at the origin. This exponential model generally provides a good fit to available experimental data. Simulations show that estimators based on the model perform well when the response is non-linear and remain reasonably efficient when the response is linear.     

On the improved estimation of a function of the scale parameter of an exponential distribution based on doubly censored sample

In the present article, we have studied the estimation of entropy, that is, a function of scale parameter ln⁡σ of an exponential distribution based on doubly censored sample when the location parameter is restricted to positive real line. The estimation problem is studied under a general class of bowl-shaped non monotone location invariant loss functions. It is established that the best affine equivariant estimator (BAEE) is inadmissible by deriving an improved estimator. This estimator is non-smooth. Further, we have obtained a smooth improved estimator. A class of estimators is considered and sufficient conditions are derived under which these estimators improve upon the BAEE. In particular, using these results we have obtained the improved estimators for the squared error and the linex loss functions. Finally, we have compared the risk performance of the proposed estimators numerically. One data analysis has been performed for illustrative purposes.     

An effective estimation strategy for population mean under random non-response situations in two-phase successive sampling

This paper presents a modified exponential type estimation strategy for the current population mean in the presence of random non-response situations in two-occasion successive sampling under two-phase set-up. The properties of the proposed estimators have been examined with the assumption that numbers of sampling units follow a distribution due to random non-response. The performances of the proposed estimators are compared with the estimators designated for the complete response situations. Empirical studies are carried out to show the dominance nature of the proposed estimators over the estimator defined for complete response situations. Appropriate recommendations have been made to the survey practitioners/researchers for their real-life practical applications.