Research paper | 31-October-2017
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
Zoramthanga Ralte,
Gitasree Das
Statistics in Transition New Series, Volume 16 , ISSUE 2, 183–202
Research Communicate | 27-May-2018
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
Shakti Prasad
Statistics in Transition New Series, Volume 19 , ISSUE 1, 159–166
Sampling Methods | 22-July-2018
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
Mir Subzar,
Showkat Maqbool,
Tariq Ahmad Raja,
Surya Kant Pal,
Prayas Sharma
Statistics in Transition New Series, Volume 19 , ISSUE 2, 219–238
Article | 20-December-2020
This paper deals some linear regression type ratio exponential estimators for estimating the population mean using the known values of quartile deviation and deciles of an auxiliary variable in survey sampling. The expressions of the bias and the mean square error of the suggested estimators have been derived. It was compared with the usual mean, usual ratio (Cochran (1977)), Kadilar and Cingi (2004, 2006) and Subzar et al. (2017) estimators. After comparison, the condition which makes the
Shakti Prasad
Statistics in Transition New Series, Volume 21 , ISSUE 5, 85–98
Research Article | 04-September-2019
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, Volume 20 , ISSUE 3, 81–95
Article | 13-December-2019
The most dominant problem in the survey sampling is to obtain the better ratio estimators for the estimation of population mean or population variance. Estimation theory is enhanced by using the auxiliary information in order to improve on designs, precision and efficiency of estimators. A modified class of ratio estimator is suggested in this paper to estimate the population mean. Expressions for the bias and the mean square error of the proposed estimators are obtained. Both analytical and
Mir Subzar,
S. Maqbool,
T. A. Raja,
Prayas Sharma
Statistics in Transition New Series, Volume 20 , ISSUE 4, 181–189
Article | 27-May-2019
;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,
Manish Trivedi,
Konda Satish
Statistics in Transition New Series, Volume 20 , ISSUE 1, 87–102
Article | 24-August-2017
Nazeema T. Beevi,
C. Chandran
Statistics in Transition New Series, Volume 18 , ISSUE 2, 227–245
Article | 06-April-2018
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.
Li Cunfang,
Zhang Taohong,
Zhang Dezheng,
Wang Huan,
Zeng Qingfeng,
Sun Yi
International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 1, 14–24
Research paper | 30-October-2017
A. K. P. C. Swain,
Manjula Das
Statistics in Transition New Series, Volume 16 , ISSUE 1, 37–52
Article | 15-March-2019
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
Halim Zeghdoudi,
Lazri Nouara,
Djabrane Yahia
Statistics in Transition New Series, Volume 19 , ISSUE 4, 671–692
Research Communicate | 18-March-2020
Tolga Zaman
Statistics in Transition New Series, Volume 21 , ISSUE 1, 159–168
Sampling Methods | 22-July-2018
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
Siraj Muneer,
Javid Shabbir,
Alamgir Khalil
Statistics in Transition New Series, Volume 19 , ISSUE 2, 259–276
Article | 06-July-2017
G. N. Singh,
M. Khetan,
S. Maurya
Statistics in Transition New Series, Volume 17 , ISSUE 2, 163–182
Article | 13-December-2019
Rajesh Singh,
Madhulika Mishra
Statistics in Transition New Series, Volume 20 , ISSUE 4, 89–111
Article | 13-June-2021
Brij Behari Khare,
Ashutosh Ashutosh,
Piyush Kant Rai
Statistics in Transition New Series, Volume 22 , ISSUE 2, 189–200
Research Article | 01-March-2017
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,
Rosdiadee Nordin,
Mahamod Ismail
International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 1, 124–145
Article | 27-December-2017
, 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,
Norlela Ishak,
Ramli Adnan,
Yahaya Md. Sam,
Mazidah Tajjudin,
Mohd Hezri Fazalul Rahiman
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 1, 32–48
Article | 15-March-2019
G. N. Singh,
Amod Kumar,
Gajendra K. Vishwakarma
Statistics in Transition New Series, Volume 19 , ISSUE 4, 575–596
Article | 01-June-2016
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.
Swathi Ramasahayam,
Shubhajit Roy Chowdhury
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 449–467
Article | 15-March-2019
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.
V. Ranjbar,
M. Alizadeh,
G. G. Hademani
Statistics in Transition New Series, Volume 19 , ISSUE 4, 621–643
Article | 16-December-2013
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,
Zuraida Muhammad,
Mohd Hezri Fazalul Rahiman,
Mohd Nasir Taib
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 5, 2055–2074
Research Article | 01-June-2020
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.
Ramajeyam Tharshan,
Pushpakanthie Wijekoon
Statistics in Transition New Series, Volume 21 , ISSUE 2, 89–117
Research Article | 08-December-2021
estimator, and the Anderson–Darling estimator. We derive analytical forms for the bias and mean square error. A simulation study is performed to investigate the consistency of the suggested methods of estimation. Data relating to the wind speed and service times of aircraft windshields are used with the studied methods. The simulation studies and real data applications have revealed that the maximum likelihood estimator performs more efficiently than its remaining counterparts.
Amal S. Hassan,
Salwa M. Assar,
Kareem A. Ali,
Heba F. Nagy
Statistics in Transition New Series, Volume 22 , ISSUE 4, 171–189
Article | 03-March-2021
Zaman and Bulut (2018a) developed a class of estimators for a population mean utilising LMS robust regression and supplementary attributes. In this paper, a family of estimators is proposed, based on the adaptation of the estimators presented by Zaman (2019), followed by the introduction of a new family of regression-type estimators utilising robust regression tools (LAD, H-M, LMS, H-MM, Hampel-M, Tukey-M, LTS) and supplementary attributes. The mean square error expressions of the adapted and
Irsa Sajjad,
Muhammad Hanif,
Nursel Koyuncu,
Usman Shahzad,
Nadia H. Al-Noor
Statistics in Transition New Series, Volume 22 , ISSUE 1, 207–216
Article | 06-July-2017
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,
Partha Lahiri
Statistics in Transition New Series, Volume 17 , ISSUE 3, 429–447
Article | 30-November-2018
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:
(2)
L=−∑i=1nyilog(y^i)#
In addition to the above improvements, we will introduce four optimization algorithms, SGD (with momentum), Adam, Adamax, and RMSprop.
B.
Comparison effects of different
Haoqi Yang,
Hongge Yao
International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 3, 47–52
Article | 01-September-2015
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.
Arun Jana,
Nabarun Bhattacharyya,
Rajib Bandyopadhyay,
Bipan Tudu,
Subhankar Mukherjee,
Devdulal Ghosh,
Jayanta Kumar Roy
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1730–1747
Article | 05-June-2013
. 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
Qaisar Hayat,
Fan Li-Yun,
Xiu-Zhen Ma,
Tian Bingqi
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 3, 1077–1101
Article | 20-July-2021
between the planned and actual outcomes were reported using the root mean square error (RMSE). The number of cases that exceeded limits set for clinical significance, the direction of the error in relation to the direction of planned movement and the differences between segmental and non-segmental procedures were evaluated as secondary outcomes. Results: The largest translational RMSE was 1.53 mm along the y-axis in the maxilla and 1.34 mm along the y-axis in the mandible. The largest rotational RMSE
Richard Lee,
Mithran S. Goonewardene,
Ajmal Mian,
Brent Allan,
Danny Brock,
Michelle Trevenen
Australasian Orthodontic Journal, Volume 34 , ISSUE 1, 17–26