The problem of estimation of finite population mean on the current occasion based on the samples selected over two occasions has been considered. In this paper, first a chain ratio-to-regression estimator was proposed to estimate the population mean on the current occasion in two-occasion successive (rotation) sampling using only the matched part and one auxiliary variable, which is available in both the occasions. The bias and mean square error of the proposed estimator is obtained. We
Statistics in Transition New Series , ISSUE 2, 183–202
In this paper, a product exponential method of imputation has been suggested and their corresponding resultant point estimator has been proposed for estimating the population mean in sample surveys. The expression of bias and the mean square error of the suggested estimator has also been derived, up to the first order of large sample approximations. Compared with the mean imputation method, Singh and Deo (Statistical Papers (2003)) and Adapted estimator (Bahl and Tuteja (1991)), the simulation
Statistics in Transition New Series , ISSUE 1, 159–166
In the present study we have proposed an improved family of estimators for estimation of population mean using the auxiliary information of median, quartile deviation, Gini’s mean difference, Downton’s Method, Probability Weighted Moments and their linear combinations with correlation coefficient and coefficient of variation. The performance of the proposed family of estimators is analysed by mean square error and bias and compared with the existing estimators in the literature. By
Tariq Ahmad Raja,
Surya Kant Pal,
Statistics in Transition New Series , ISSUE 2, 219–238
of a larger first phase sample. In this situation, a class of two phase sampling estimators for estimating P is suggested using multi-auxiliary characters with unknown population means in the presence of non-response. The expressions of bias and mean square error of all the proposed estimators are derived and their properties are studied. An empirical study using real data sets is given to justify the theoretical considerations.
B. B. Khare,
R. R. Sinha
Statistics in Transition New Series , ISSUE 3, 81–95
;kernel function” as described in the introduction. We investigated the following results of the smoothed estimator under the non-i.i.d. set-up such as (a) its small sample behaviour is compared with the unsmoothed version (BJ estimator) based on their mean square errors by using Monte-Carlo simulation, and established the percentage gain in precision of smoothed estimator over its unsmoothed version measured in terms of their mean square error, (b) its large sample properties such as almost
Y. S. Ramakrishnaiah,
Statistics in Transition New Series , ISSUE 1, 87–102
Nazeema T. Beevi,
Statistics in Transition New Series , ISSUE 2, 227–245
learning machine which introduces penalty function. CPM uses the least square method to realize the combination of PM and CM and gets the value of the coefficient. Compare the actual data on ball mill to the data of the model then the result shows that the mean square error of CPM is smaller than the mean square error of PM and CM. The experimental results validate the effectiveness of the proposed method, which can be effectively used in ball mill in our industry.
International Journal of Advanced Network, Monitoring and Controls , ISSUE 1, 14–24
A. K. P. C. Swain,
Statistics in Transition New Series , ISSUE 1, 37–52
In this paper, we introduce a new Lindley Pareto distribution, which offers a more flexible model for modelling lifetime data. Some of its mathematical properties like density function, cumulative distribution, mode, mean, variance, and Shannon entropy are established. A simulation study is carried out to examine the bias and mean square error of the maximum likelihood estimators of the unknown parameters. Three real data sets are fitted to illustrate the importance and the flexibility of the
Statistics in Transition New Series , ISSUE 4, 671–692
In this article, we propose a class of generalized exponential type estimators to estimate the finite population mean by using two auxiliary variables under non-response in simple random sampling. The proposed estimator under non-response in different situations has been studied and gives minimum mean square error as compared to all other considered estimators. Usual exponential ratio type estimator, exponential product type estimator and many more estimators are also identified from the
Statistics in Transition New Series , ISSUE 2, 259–276
Statistics in Transition New Series , ISSUE 1, 159–168
This study evaluates the consistency between the bicycle torque of the proposed system, and a Schoberer Rad Messtechnik (SRM) system. The torque was measured while a trainer was cycling indoors, and the measured values were compared with those of the SRM system. A Bland-Altman statistical analysis indicated that the measured values agreed with the SRM within 95%. The mean absolute percentage error and root mean square error between the measurements of the proposed system and the SRM system were
Sadik Kamel Gharghan,
International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 124–145
G. N. Singh,
Statistics in Transition New Series , ISSUE 2, 163–182
Statistics in Transition New Series , ISSUE 4, 89–111
, the derivative order of FO model is estimated. These models has been validated by comparison of error, coefficient of determination (R2), mean square error (MSE) and correlation function. The results for the proposed model show improvement compared to the IO model.
Nuzaihan Mhd Yusof,
Yahaya Md. Sam,
Mohd Hezri Fazalul Rahiman
International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 32–48
spectrometer. Based on the theoretical analysis the optimal wavelength of sensor is found to be 995nm for obtaining proper Photo plethysmograph (PPG). The regression analysis has been carried out on PPG signal with the artificial neural networks for obtaining a prediction model for estimating the blood urea concentration. The mean square error of prediction is found to be ± 2.23mg/dL.
Shubhajit Roy Chowdhury
International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 449–467
random sample from this distribution are investigated. A simulation study is performed to compare the performance of the different parameter estimates in terms of bias and mean square error. We apply a real data set to illustrate the applicability of the new model. Empirical findings show that proposed model provides better fits than other well-known extensions of Lindley distributions.
G. G. Hademani
Statistics in Transition New Series , ISSUE 4, 621–643
G. N. Singh,
Gajendra K. Vishwakarma
Statistics in Transition New Series , ISSUE 4, 575–596
25 rules and PID controller is measured by using performance indices of settling time, rise time, percentage overshoot (%OS) and root mean square error. The step responses analysis and robustness test show that STFPID and PID controller are able to drive the steam temperature to the desired set point However, the analysis shows that STFPID produces better performances based on set point tracking and adoption of load disturbance.
Zakiah Mohd Yusoff,
Mohd Hezri Fazalul Rahiman,
Mohd Nasir Taib
International Journal on Smart Sensing and Intelligent Systems , ISSUE 5, 2055–2074
Lindley distributions as special cases. Various structural properties of the new distribution are discussed and the size-biased and the length-biased are derived. A simulation study is conducted to examine the mean square error for the parameters by means of the method of maximum likelihood. Finally, simulation studies and some real-world data sets are used to illustrate its ﬂexibility in terms of its location, scale and shape parameters.
Statistics in Transition New Series , ISSUE 2, 89–117
correct the bias of the naive predictor using a double sampling idea where both inaccurate and accurate measurements are taken on the binary variable for all the units of a sample drawn from the original data using a probability sampling scheme. Using this additional information and design-based sample survey theory, we derive a biascorrected predictor. We examine the cases where the new bias-corrected predictors can also improve over the naive predictor in terms of mean square error (MSE).
Noriah M. Al-Kandari,
Statistics in Transition New Series , ISSUE 3, 429–447
value of the ith sample, and yi is the labelof the ith sample. In the case of back-propagation by the gradient descent method, the minimum mean square error is easy to occur when the neuron output is close to ‘1’ and the gradient is too small to learn slow. We use the cross-entropy loss function here:
In addition to the above improvements, we will introduce four optimization algorithms, SGD (with momentum), Adam, Adamax, and RMSprop.
Comparison effects of different
International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 47–52
Analysis (LDA) for identification of different rice varieties. Finally, for aroma quantifying, pure-quadratic response surface methodology model used with mean square error (MSE) 0.0028.
Jayanta Kumar Roy
International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1730–1747
. Lab experiments have been conducted to measure the pump side and injector side pressures by using KISTLER 4067 piezoresistive pressure sensors under controlled environment. Each model has been verified by comparing its simulated results with those of experimentally verified AMESim numerical model of CEUP system. Model evaluation statistical techniques like “Root Mean Square Error” (RMSE) and “Index of Agreement” (IA) have been used to quantify the predicted results of
International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1077–1101