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In this paper, separate and combined ratio type estimators have been proposed in presence of non-response for estimating the population mean under stratified random sampling when the non-response occurs both on study and the auxiliary variables and the population mean of the auxiliary variable is unknown. The expressions for the biases and mean square errors (MSEs) of the proposed estimators have been derived to the first order of approximation. The proposed estimators have been compared with the other existing estimators using MSE criterion, and the condition under which the proposed estimators perform better than existing estimators have been obtained. In addition to the theoretical research, an empirical study was conducted. [ABSTRACT FROM AUTHOR]

In this article, a class of Poisson-regression based estimators has been proposed for estimating the finite population mean in simple random sampling without replacement (SRSWOR). The Poisson-regression model is the most common method used to model count responses in many studies. The expression for bias and mean square error (MSE) of proposed class of estimators are obtained up to first order of approximation. The proposed estimators have been compared theoretically with the existing estimators, and the condition under which the proposed class of estimators perform better than existing estimators have been obtained. Two real data sets are considered to assess the performance of the proposed estimators. Numerical findings confirms that the proposed estimators dominate over the existing estimators such as Koc (2021) and Usman et al. (2021) in terms of mean squared error. [ABSTRACT FROM AUTHOR]

In the current investigation, we have suggested a Difference Cum-Exponential Type Efficient Estimator of Population variance of the study variable using information on the auxiliary variable. Up to the first order of approximation, the proposed estimator's bias and mean square error (MSE) expressions are derived and suggested optimum estimator is also found, with its optimal qualities are investigated. The suggested estimator is proven to be more competent than sample variance, classic ratio estimators based on Isaki, Singh et al. and Kadilar and Cingi estimators in [1-3]. Numerical study is also carried out by using real data sets. [ABSTRACT FROM AUTHOR]

In the present investigation, a methodology has been developed for obtaining Approximately Optimum Strata Boundaries (AOSB), appropriate for surveys involving single study variable (Y), on the basis of two auxiliary variables (X and Z) closely related to the study variable. For theoretical development, regression model has been considered as Y = C (X, Z) + e, where C (X, Z) is a function of X & Z and 'e' is error term. Minimal equations have been obtained, under certain assumptions, by minimizing the variance of the estimation variable. Due to implicit nature of these equations, a Cum 3... rule has been proposed for finding out AOSB. Comparisons have been made empirically, using certain density functions, with cube root method due to Singh and Sukhatme (1969) for single auxiliary variable. It showed remarkable gain in efficiency in case two auxiliary variables are used as the basis of stratification. [ABSTRACT FROM AUTHOR]

In this paper, we have taken under considerations two auxiliary variables used as stratification variables and single study variable. For obtaining the optimum strata boundaries, we have solved the non linear equations using dynamic programming that resulted in more efficient results rather than obtained on using single study variable and single auxiliary variable. The proposed method is illustrated by using different distributions followed by stratification variables. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed an approach for obtaining optimum strata boundaries for one dependent variable with two auxiliary variables. The problem is formulated as non-linear programming problem which is solved by using dynamic programming. While comparing the proposed approach to the approaches already developed for single study variable and single auxiliary variable more precision is obtained in case of the proposed approach. Empirical study has been given in which each of the auxiliary variable is supposed to follow different distribution. [ABSTRACT FROM AUTHOR]

Ordinary least square method (OLS) is an efficient method for estimating the parameters under the presence of assumptions of error term. Moreover, the problems like multicollinearity, auto-correlation, outliers, influential observations, heteroscedasticity etc., do occur and then OLS estimates are less efficient. Under these conditions, quantile regression technique is advisable, as it does not require any distributional assumption about error term. In this paper, an attempt has been made to propose a quantile regression model using Cobb-Douglas production function for analyzing decadal data of foodgrain production as endogenous variable with respect to net irrigated area, net sown area, net area under cultivation, consumption of fertilizers, consumption of pesticides and consumption of electricity in agriculture as exogenous variables. The behaviour of the data indicated that some observations were outliers and influential observations. Elasticity of production and marginal value productivity has also been obtained and the best model has been selected on the basis of sign, size and significance of the parameters. Moreover, on the basis of elasticity of production, marginal value productivity and quantile 0.90th regression, net sown area and area under cultivation played a vital role in increasing foodgrain production. [ABSTRACT FROM AUTHOR]

One of the main reasons for stratifying the population is to produce a gain in precision of the estimates, in the sample surveys. For achieving this, one of the problem is determination of optimum strata boundaries. The strata boundaries should be obtained in such a way, so that it can reasonably expect to reduce the cost of the survey as much as possible without sacrificing the accuracy or alternatively, reducing the margin of error to the greatest possible extent for the same expected cost. In this paper, we have discussed the way of obtaining optimum strata boundaries when the cost of every unit varies in the whole strata. The problem is formulated as non-linear programming problem which is solved by using Bellman's principle of optimality. For numerical illustration an example is presented for uniformly distributed study variable. [ABSTRACT FROM AUTHOR]

A study was conducted in Jammu region of Jammu and Kashmir State to determine the species composition and relative abundance of pollinators on Parkinsonia aculeata L., (Family Fabaceae) is a perennial flowering plant, growing as an avenue tree on roadsides. Parkinsonia flowers attracted 27 species of insects belonging to orders Hymenoptera, Diptera and Lepidoptera. They included Megachile bicolor (Fabricius), Megachile hera (Bingham), Megachile lanata (Fabricius), Megachile disjuncta (Fab.), Megachile cephalotes (Smith), Megachile badia (Fab.), Megachile semivestita (Smith), Megachile vigilans (Smith), Megachile relata (Fab.), Megachile femorata, Andrena sp., Amegilla zonata (Linnaeus), Amegilla confusa (Smith), Apis dorsata (Fab.), Apis cerana (Fab.), Apis florea (Fab.), Ceratina smaragdula (Fab.)., Xylocopa latipes (Drury), Nomia iridescens (Smith), Nomia curvipes (Fab.), and seven species of unidentified insects. Megachile bees were most abundant and constituted more than 95% of the insects visiting Parkinsonia aculeata flowers. Species diversity measured by Shannon Wiener index showed a high value of H' = 2.03, reflecting a diverse pollinator community in the area. The foragers of all the species were found to be most active between 11.00 and 15.00 hrs and the population of flower visitors declined thereafter. Information on diversity of native pollinators from disturbed habitats and their specific dependence on P. aculeata during dearth period will provide a base for conservation of pollinators. [ABSTRACT FROM AUTHOR]

In this article we have proposed some new improved ratio estimators based on robust regression that are robust against outliers and provide reliable results even when outliers are present; the properties are also investigated. The proposed class of estimators has been shown to be more effective than the current classes of estimators. An empirical analysis was conducted to see how well the proposed class of estimators compared to others. [ABSTRACT FROM AUTHOR]

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]

Nowadays we deal with Big-Data commonly. The users of statistics rely on having a large sample size n for using the statistical methods based on normality. Usual inference methods are typically based on considering the Normal as the limit distributions of the sample mean for a large n. With large enough sample sizes (> 30 or 40), the violation of the normality assumption should not cause major problems. This fact implies that we can use parametric procedures even when the data are not normally distributed. Al least a goodness-of-fit test must be performed for accepting whether normality is valid or not. Monte Carlo (MC) techniques are used for selecting independent random samples of populations of means of three variables of importance in web network management. Different tests are performed to establish the acceptance of the normality. We did not find reliable results even for samples of size 10 000. [ABSTRACT FROM AUTHOR]