Kernel method for the estimation of the distribution function and the mean with auxiliary information in ranked set sampling

2002 ◽  
Vol 13 (4) ◽  
pp. 397-406 ◽  
Author(s):  
K. F. Lam ◽  
Philip L. H. Yu ◽  
C. F. Lee
Keyword(s):  
Distribution Function ◽  
Kernel Method ◽  
Auxiliary Information ◽  
Ranked Set Sampling ◽  
The Mean

scholarly journals Exponentially Weighted Moving Average Control Charts for the Process Mean Using Exponential Ratio Type Estimator

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hina Khan ◽  
Saleh Farooq ◽  
Muhammad Aslam ◽  
Masood Amjad Khan
Keyword(s):  
Control Charts ◽  
Sampling Design ◽  
Control Structure ◽  
Moving Average ◽  
Auxiliary Information ◽  
Ranked Set Sampling ◽  
Process Mean ◽  
Average Control ◽  
The Mean ◽  
Weighted Moving Average

This study proposes EWMA-type control charts by considering some auxiliary information. The ratio estimation technique for the mean with ranked set sampling design is used in designing the control structure of the proposed charts. We have developed EWMA control charts using two exponential ratio-type estimators based on ranked set sampling for the process mean to obtain specific ARLs, being suitable when small process shifts are of interest.

scholarly journals Neutrosophic ratio-type estimators for estimating the population mean

2021 ◽  
Author(s):  
Zaigham Tahir ◽  
Hina Khan ◽  
Muhammad Aslam ◽  
Javid Shabbir ◽  
Yasar Mahmood ◽  
...  
Keyword(s):  
Minimum Mean Square Error ◽  
Auxiliary Information ◽  
Population Parameter ◽  
Uncertain Information ◽  
Population Mean ◽  
Classical Statistics ◽  
Neutrosophic Statistics ◽  
The Mean ◽  
Type Data ◽  
First Time

AbstractAll researches, under classical statistics, are based on determinate, crisp data to estimate the mean of the population when auxiliary information is available. Such estimates often are biased. The goal is to find the best estimates for the unknown value of the population mean with minimum mean square error (MSE). The neutrosophic statistics, generalization of classical statistics tackles vague, indeterminate, uncertain information. Thus, for the first time under neutrosophic statistics, to overcome the issues of estimation of the population mean of neutrosophic data, we have developed the neutrosophic ratio-type estimators for estimating the mean of the finite population utilizing auxiliary information. The neutrosophic observation is of the form $${Z}_{N}={Z}_{L}+{Z}_{U}{I}_{N}\, {\rm where}\, {I}_{N}\in \left[{I}_{L}, {I}_{U}\right], {Z}_{N}\in [{Z}_{l}, {Z}_{u}]$$ Z N = Z L + Z U I N where I N ∈ I L , I U , Z N ∈ [ Z l , Z u ] . The proposed estimators are very helpful to compute results when dealing with ambiguous, vague, and neutrosophic-type data. The results of these estimators are not single-valued but provide an interval form in which our population parameter may have more chance to lie. It increases the efficiency of the estimators, since we have an estimated interval that contains the unknown value of the population mean provided a minimum MSE. The efficiency of the proposed neutrosophic ratio-type estimators is also discussed using neutrosophic data of temperature and also by using simulation. A comparison is also conducted to illustrate the usefulness of Neutrosophic Ratio-type estimators over the classical estimators.

scholarly journals Modeling of temporal groundwater level variations based on a Kalman filter adaptation algorithm with exogenous inputs

2016 ◽  
Vol 19 (2) ◽  
pp. 191-206 ◽  
Author(s):  
Emmanouil A. Varouchakis
Keyword(s):  
Kalman Filter ◽  
Groundwater Level ◽  
Auxiliary Information ◽  
Model Parameters ◽  
Adaptation Algorithm ◽  
Arx Model ◽  
Resources Management ◽  
Temporal Modelling ◽  
The Mean ◽  
First Time

Reliable temporal modelling of groundwater level is significant for efficient water resources management in hydrological basins and for the prevention of possible desertification effects. In this work we propose a stochastic method of temporal monitoring and prediction that can incorporate auxiliary information. More specifically, we model the temporal (mean annual and biannual) variation of groundwater level by means of a discrete time autoregressive exogenous variable (ARX) model. The ARX model parameters and its predictions are estimated by means of the Kalman filter adaptation algorithm (KFAA) which, to our knowledge, is applied for the first time in hydrology. KFAA is suitable for sparsely monitored basins that do not allow for an independent estimation of the ARX model parameters. We apply KFAA to time series of groundwater level values from the Mires basin in the island of Crete. In addition to precipitation measurements, we use pumping data as exogenous variables. We calibrate the ARX model based on the groundwater level for the years 1981 to 2006 and use it to predict the mean annual and biannual groundwater level for recent years (2007–2010). The predictions are validated with the available annual averages reported by the local authorities.

scholarly journals On the Dynamics of the Solar Convection Zone

1980 ◽  
Vol 51 ◽  
pp. 15-16
Author(s):  
Bernard R. Durney ◽  
Hendrik C. Spruit
Keyword(s):  
Distribution Function ◽  
Energy Equation ◽  
Convection Zone ◽  
Turbulent Viscosity ◽  
Rotating Stars ◽  
Solar Convection Zone ◽  
Solar Convection ◽  
Convective Motions ◽  
The Mean ◽  
Convective Cells

AbstractWe derive expressions for the turbulent viscosity and turbulent conductivity applicable to convection zones of rotating stars. We assume that the dimensions of the convective cells are known and derive a simple distribution function for the turbulent convective velocities under the influence of rotation. From this distribution function (which includes, in particular, the stabilizing effect of rotation on convection) we calculate in the mixing-length approximation: i) the turbulent Reynolds stresstensor and ii) the expression for the heat flux in terms of the superadiabatic gradient. The contributions of the turbulent convective motions to the mean momentum and energy equation are treated consistently, and assumptions about the turbulent viscosity and heat transport are replaced by assumptions about the turbulent flow itself. The free parameters in our formalism are the relative cell sizes and their dependence on depth and latitude.

NOTE ON THE VLASOV EQUATION FOR PLASMAS

1963 ◽  
Vol 41 (12) ◽  
pp. 1960-1966 ◽  
Author(s):  
Ta-You Wu ◽  
M. K. Sundaresan
Keyword(s):  
Distribution Function ◽  
Fourier Transform ◽  
Vlasov Equation ◽  
Hermite Polynomials ◽  
Infinite Matrix ◽  
Mean Square ◽  
Initial Value ◽  
Positive Ions ◽  
The Mean ◽  
The Fourier Transform

The linearized Vlasov equation is solved as an initial value problem by expanding (the Fourier components of) the distribution function in a series of Hermite polynomials in the momentum, with coefficients which are functions of time. The spectrum of frequencies is given by the eigenvalues of an infinite matrix. All the frequencies ω are real, extending from small values of order ω2 = k2(u22), where (u22) is the mean square velocity of the positive ions (of mass M), to [Formula: see text], where ω1, (u12) are the plasma frequency and mean square velocity of the electrons (of mass m). The classic work of Landau solves the Vlasov equation for (the Fourier transform of) the potential for which he obtains the "damping", whereas Van Kampen and the present writers solve the equation for (the Fourier transform of) the distribution function itself. While the present work gives results equivalent to those of Van Kampen, the method is simpler and in fact elementary.

Ratio-Type Estimation Using Scrabled Auxiliary Variables in Stratification Under Simple Random Sampling and Ranked Set Sampling

2022 ◽  
pp. 62-85
Author(s):  
Carlos N. Bouza-Herrera ◽  
Jose M. Sautto ◽  
Khalid Ul Islam Rather
Keyword(s):  
Random Sampling ◽  
Mean Squared Error ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Auxiliary Variables ◽  
Mild Conditions ◽  
Squared Error ◽  
The Mean ◽  
Better Than

This chapter introduced basic elements on stratified simple random sampling (SSRS) on ranked set sampling (RSS). The chapter extends Singh et al. results to sampling a stratified population. The mean squared error (MSE) is derived. SRS is used independently for selecting the samples from the strata. The chapter extends Singh et al. results under the RSS design. They are used for developing the estimation in a stratified population. RSS is used for drawing the samples independently from the strata. The bias and mean squared error (MSE) of the developed estimators are derived. A comparison between the biases and MSEs obtained for the sampling designs SRS and RSS is made. Under mild conditions the comparisons sustained that each RSS model is better than its SRS alternative.

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