Wyniki wyszukiwania - Biblioteka Nauki

1

Estimation of Finite Population Mean Using Deciles of an Auxiliary Variable

100%

Subramani J. , Kumarapandiyan G.

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tom 14

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nr 1

75-88

EN

The present paper deals with a class of modified ratio estimators for estimation of population mean of the study variable when the population deciles of the auxiliary variable are known. The biases and the mean squared errors of the proposed estimators are derived and compared with that of existing modified ratio estimators for certain known populations. Further, we have also derived the conditions for which the proposed estimators perform better than the existing modified ratio estimators. From the numerical study it is also observed that the proposed modified ratio estimators perform better than the existing modified ratio estimators.

2

Almost Unbiased Ratio and Product Type Exponential Estimators

100%

Yadav R. , Upadhyaya L. N. , Singh H. P. , Chatterjee S.

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tom 13

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nr 3

537-550

EN

This paper considers the problem of estimating the population mean Y of the study variate y using information on auxiliary variate x. We have suggested a generalized version of Bahl and Tuteja (1991) estimator and its properties are studied. It is found that asymptotic optimum estimator (AOE) in the proposed generalized version of Bahl and Tuteja (1991) estimator is biased. In some applications, biasedness of an estimator is disadvantageous. So applying the procedure of Singh and Singh (1993) we derived an almost unbiased version of AOE. A numerical illustration is given in the support of the present study.

3

Improved separate ratio and product exponential type estimators in the case of post-stratification

100%

Lone H. A. , Tailor R.

Statistics in Transition new series

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2015

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tom 16

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nr 1

53–64

EN

This paper addressed the problem of estimation of finite population mean in the case of post-stratification. Improved separate ratio and product exponential type estimators in the case of post-stratification are suggested. The biases and mean squared errors of the suggested estimators are obtained up to the first degree of approximation. Theoretical and empirical studies have been done to demonstrate better efficiencies of the suggested estimators than other considered estimators.

4

Noise and bias - some controversies raised by the book 'Noise: A Flaw in Human Judgment', written by Daniel Kahneman, Olivier Sibony, Cass R. Sunstein

100%

Szreder M.

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2022

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tom 69

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nr 1

39-49

EN

The paper reviews and discusses the statistical aspects of the phenomenon called 'noise' which Daniel Kahneman, the Nobel Prize winning psychologist, and his colleagues present in their new book entitled 'Noise: A Flaw in Human Judgment'. Noise is understood by the authors as an unexpected and undesirable variation present in people's judgments. The variability of judgments influences decisions which are made on the basis of those judgments and, consequently, may have a negative impact on the operations of various institutions. This is the main concern presented and analyzed in this book. The objective of this paper is to look at the relationship between bias and noise - the two major components of the mean squared error (MSE) - from a different perspective which is absent in the book. Although the author agrees that each of the two components contributes equally to MSE, he claims that in some circumstances a reduction of noise can make accurate inference not less, but more difficult. It is justified that the actual impact of noise cannot be accurately determined without considering both bias and noise simultaneously.

5

Single-tone frequency estimation based on reformed covariance for half-length autocorrelation

100%

Sienkowski S. , Krajewski M.

Metrology and Measurement Systems

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2020

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tom Vol. 27, nr 3

473--493

EN

This paper presents a new simple and accurate frequency estimator of a sinusoidal signal based on the signal autocorrelation function (ACF). Such an estimator was termed as the reformed covariance for half-length autocorrelation (RC-HLA). The designed estimator was compared with frequency estimators well-known from the literature, such as the modified covariance for half-length autocorrelation (MC-HLA), reformed Pisarenko harmonic decomposition for half-length autocorrelation (RPHD-HLA), modified Pisarenko harmonic decomposition for half-length autocorrelation (MPHD-HLA), zero-crossing (ZC), and iterative interpolated DFT (IpDFT-IR) estimators. We determined the samples of the ACF of a sinusoidal signal disturbed by Gaussian noise (simulations studies) and the samples of the ACF of a sinusoidal voltage (experimental studies), calculated estimators based on the obtained samples, and computed the mean squared error (MSE) to compare the estimators. The errors were juxtaposed with the Cramér-Rao lower bound (CRLB). The research results have shown that the proposed estimator is one of the most accurate, especially for SNR>25dB. Then the RC-HLA estimator errors are comparable to the MPHD-HLA estimator errors. However, the biggest advantage of the developed estimator is the ability to quickly and accurately determine the frequency based on samples collected from no more than five signal periods. In this case, the RC-HLA estimator is the most accurate of the estimators tested.

6

A Ratio-Cum-Product Estimator of Finite Population Mean in Systematic Sampling

100%

Tailor R. , Jatwa N. K. , Singh H. P.

Statistics in Transition new series

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2014

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tom 15

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nr 3

391-398

EN

In this paper we consider the problem of estimation of population mean using information on two auxiliary variables in systematic sampling. We have extended Singh (1967) estimator for estimation of population mean in systematic sampling. We have derived the expressions for the bias and mean squared error of the suggested estimator up to the first degree of approximation. We have compared the suggested estimator with existing estimators and obtained the conditions under which the suggested estimator is more efficient. An empirical study has been carried out to demonstrate the performance of the suggested estimator.

7

Efficient two-parameter estimator in linear regression model

100%

Dorugade A. V.

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tom 20

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nr 2

173-185

EN

In this article, two-parameter estimators in linear model with multicollinearity are considered. An alternative efficient two-parameter estimator is proposed and its properties are examined. Furthermore, this was compared with the ordinary least squares (OLS) estimator and ordinary ridge regression (ORR) estimators. Also, using the mean squares error criterion the proposed estimator performs more efficiently than OLS estimator, ORR estimator and other reviewed two-parameter estimators. A numerical example and simulation study are finally conducted to illustrate the superiority of the proposed estimator.

8

Efficient estimation of population mean in the presence of non-response and measurement error

88%

Tiwari K. K. , Sharma V.

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tom 24

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nr 3

95-116

EN

In real-world surveys, non-response and measurement errors are common, therefore studying them together seems rational. Some population mean estimators are modified and studied in the presence of non-response and measurement errors. Bias and mean squared error expressions are derived under different cases. For all estimators, a theoretical comparison is made with the sample mean per unit estimator. The Monte-Carlo simulation is used to present a detailed picture of all estimators' performance.

9

A New Multi-Step Backward Cloud Transformation Algorithm Based on Normal Cloud Model

88%

Xu C. , Wang G. , Zhang Q.

Fundamenta Informaticae

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2014

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tom Vol. 133, nr 1

55--85

EN

The representation and processing of uncertainty information is one of the key basic issues of the intelligent information processing in the face of growing vast information, especially in the era of network. There have been many theories, such as probability statistics, evidence theory, fuzzy set, rough set, cloud model, etc., to deal with uncertainty information from different perspectives, and they have been applied into obtaining the rules and knowledge from amount of data, for example, data mining, knowledge discovery, machine learning, expert system, etc. Simply, This is a cognitive transformation process from data to knowledge (FDtoK). However, the cognitive transformation process from knowledge to data (FKtoD) is what often happens in human brain, but it is lack of research. As an effective cognition model, cloud model provides a cognitive transformation way to realize both processes of FDtoK and FKtoD via forward cloud transformation (FCT) and backward cloud transformation (BCT). In this paper, the authors introduce the FCT and BCT firstly, and make a depth analysis for the two existing single-step BCT algorithms. We find that these two BCT algorithms lack stability and sometimes are invalid. For this reason we propose a new multi-step backward cloud transformation algorithm based on sampling with replacement (MBCT-SR) which is more precise than the existing methods. Furthermore, the effectiveness and convergence of new method is analyzed in detail, and how to set the parameters m, r appeared in MBCT-SR is also analyzed. Finally, we have error analysis and comparison to demonstrate the efficiency of the proposed backward cloud transformation algorithm for some simulation experiments.

10

Single-tone frequency estimation based on reformed covariance for half-length autocorrelation

88%

Sienkowski S. , Krajewski M. , Sienkowski S. , Krajewski M.

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11

Nonlinear optical double image encryption using random-optical vortex in fractional Hartley transform domain

75%

Singh H.

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tom Vol. 47, nr 4

557--578

EN

This paper proposed an enhanced asymmetric cryptosystem scheme for optical image encryption in the fractional Hartley transform domain. Grayscale and binary images have been encrypted separately using double random phase encoding. Phase masks based on optical vortex and random phase masks have been jointly used in spatial as well as in the Fourier planes. The images to be encrypted are first multiplied by optical vortex and random phase mask and then transformed with direct and inverse fractional Hartley transform for obtaining the encrypted images. The images are recovered from their corresponding encrypted images by using the correct parameters of the fractional Hartley transform and optical vortex, whose digital implementation has been performed using MATLAB 7.6.0 (R2008a). The random phase masks, optical vortex and transform orders associated with the fractional Hartley transform are extra keys that cause difficulty to an unauthorized user. Thus, the proposed asymmetric scheme is more secure as compared to conventional techniques. The efficacy of the proposed asymmetric scheme is verified by computing the mean squared error between recovered and the original images. The sensitivity of the asymmetric scheme is also verified with encryption parameters, noise and occlusion attacks. Numerical simulation results demonstrate the effectiveness and security performance of the proposed system.

12

Power ratio cum median-based ratio estimator of finite population mean with known population median

75%

Abdullahi U. K. , Ugwuowo F. I. , Lawson N.

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tom 24

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nr 5

35-44

EN

The search for an efficient estimator of the finite population mean has been a critical problem to the sample survey research community. This study is motivated by the fact that the conducted literature review showed that no research has developed such an average ratio estimator of the population mean that would utilize both the population and the sample medians of study variable, as well as the Srivastava (1967) estimator at a time. In this paper we proposed the power ratio cum median-based ratio estimator of the finite population mean, which is a function of two ratio estimators in the form of an average. The estimator assumes the population to be homogeneous and skewed. The properties (i.e. the Bias and the Mean Squared Error - MSE) of the proposed estimator were derived alongside its asymptotically optimum MSE. We demonstrated the efficiency of the proposed estimator jointly with its efficiency conditions by comparing it to selected estimators described in the literature. Empirically, a real-life dataset from the literature and a simulation study from two skewed distributions (Gamma and Weibull) were used to examine the efficiency gain. The empirical analysis and simulation study demonstrated that the efficiency gain is significant. Hence, the practical application of the proposed estimator is recommended, especially in socio-economic surveys.

13

Optimal allocation for equal probability two-stage design

75%

Molefe W.

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tom 23

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nr 4

129-148

EN

This paper develops optimal designs when it is not feasible for every cluster to be represented in a sample as in stratified design, by assuming equal probability two-stage sampling where clusters are small areas. The paper develops allocation methods for two-stage sample surveys where small-area estimates are a priority. We seek efficient allocations where the aim is to minimize the linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. We suggest some alternative allocations with a view to minimizing the same objective. Several alternatives, including the area-only stratified design, are found to perform nearly as well as the optimal allocation but with better practical properties. Designs are evaluated numerically using Switzerland canton data as well as Botswana administrative districts data.

14

An Approximation To The Optimal Subsample Allocation For Small Areas

75%

Molefe W. B. , Shangodoyin D. K. , Clark R. G.

Statistics in Transition new series

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2015

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tom 16

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nr 2

163-182

EN

This paper develops allocation methods for stratified sample surveys in which small area estimation is a priority. We assume stratified sampling with small areas as the strata. Similar to Longford (2006), we seek efficient allocation that minimizes a linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. Unlike Longford, we define mean-squared error in a model-assisted framework, allowing a more natural interpretation of results using an intra-class correlation parameter. This allocation has an analytical form for a special case, and has the unappealing property that some strata may be allocated no sample. We derive a Taylor approximation to the stratum sample sizes for small area estimation using composite estimation giving priority to both small area and national estimation.

15

On the statistical analysis of the harmonic signal autocorrelation function

75%

Sienkowski S. , Krajewski M.

International Journal of Applied Mathematics and Computer Science

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2021

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tom Vol. 31, no. 4

729--744

EN

The article presents new tools for investigating the statistical properties of the harmonic signal autocorrelation function (ACF). These tools enable identification of the ACF estimator errors in measurements in which the triggering of the measurements is non-synchronized. This is important because in many measurement situations the initial phase of the measured signal is random. The developed tools enable testing the ACF estimator of a harmonic signal in the presence of Gaussian noise. These are the formulas on the basis of which the statistical properties of the estimator can be determined, including the bias, the variance and the mean squared error (MSE). For comparison, the article also presents the ACF statistical analysis tools used in the conditions of synchronized measurement triggering, known from the literature. Operation of the new tools is verified by simulation and experimental studies. The conducted research shows that differences between the MSE results obtained with the use of the developed formulas and those attained from simulations and experimental tests are not greater than 1 dB.

16

Ocena dokładności cyfrowej estymacji podstawowych parametrów sygnałów

63%

Sienkowski S.

Przegląd Elektrotechniczny

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2012

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tom R. 88, nr 3a

191-194

PL

Artykuł dotyczy problematyki wyznaczania błędów estymatorów i oceny niepewności estymacji podstawowych parametrów sygnałów otrzymanych na podstawie danych spróbkowanych. Do podstawowych parametrów sygnałów zaliczamy wartość średnią, średniokwadratową skuteczną, międzyszczytową i funkcję gęstości prawdopodobieństwa.

EN

The paper focuses on errors of estimators and the measurement uncertainty of basic signals parameters set with sampled data. As basic signals parameters we regard mean, mean square, root mean square, peak-to-peak amplitude and probability density function.

17

O estymacji wartości średniej napięcia sinusoidalnego

63%

Sienkowski S. , Krajewski M.

Przegląd Elektrotechniczny

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2022

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tom R. 98, nr 11

139--142

PL

W artykule oceniono dokładność wyników estymacji wartości średniej napięcia sinusoidalnego. W tym celu zastosowano estymator wartości średniej obliczany na podstawie próbek napięcia. Wyznaczono obciążenie, wariancję i błąd średniokwadratowy estymatora.

EN

The article evaluates the accuracy of the estimating results of the mean value of a sinusoidal voltage. For this purpose, a mean value estimator calculated from voltage samples has been used. The bias, the variance and the mean squared error of an estimator have been determined.

18

Numerical methods for linear minimax estimation

63%

Gaffke N. , Heiligers B.

Discussiones Mathematicae Probability and Statistics

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2000

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tom 20

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nr 1

51-62

EN

We discuss two numerical approaches to linear minimax estimation in linear models under ellipsoidal parameter restrictions. The first attacks the problem directly, by minimizing the maximum risk among the estimators. The second method is based on the duality between minimax and Bayes estimation, and aims at finding a least favorable prior distribution.

19

Comparative Analysis of Channel Estimation Techniques in SISO, MISO and MIMO Systems

51%

Obinna O. , Kennedy O. , Osemwegie O. , Nsikan N.

International Journal of Electronics and Telecommunications

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2017

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tom Vol. 63, No. 3

299--304

EN

The ever-growing need for high data rate, bandwidth efficiency, reliability, less complexity and less power consumption in our communication systems is on the increase. Modern techniques have to be developed and put in place to meet these requirements. Research has shown, that compared to conventional Single Input Single Output (SISO) systems, Multiple-Input Single Output (MISO), and Multiple-Input Multiple-Output (MIMO) can actually increase the data rate of a communication system, without actually requiring more transmit power or bandwidth. This paper aims at the investigation of the existing channel estimation techniques. Based on the pilot arrangement, the block type and comb type are compared, employing the Least Square estimation (L.S) and Minimum Mean Squared Error (MMSE) estimators. Pilots occupy bandwidth, minimizing the number of pilots used to estimate the channel, in order to allow for more bandwidth utilization for data transmission, without compromising the accuracy of the estimates is taken into consideration. Various channel interpolation techniques and pilot-data insertion ratio are investigated, simulated and compared, to determine the best performance technique with less complexity and minimum power consumption. As performance measures, the Mean Squared Error (MSE) and Bit Error Rate (BER) as a function of Signal to Noise power Ratio (SNR) of the different channel estimation techniques are plotted, in order to identify the technique with the most optimal performance. The complexity and energy efficiency of the techniques are also investigated. The system modelling and simulations are carried out using Matlab simulation package. The MIMO gives the optimum performance, followed by the MISO and SISO. This is as a result of the diversity and multiplexing gain experienced in the multiple antenna techniques using the STBC.