To see the other types of publications on this topic, follow the link: Estimators.
Journal articles on the topic 'Estimators'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Estimators.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Benkhaled, Abdelkader, Abdenour Hamdaoui, and Mekki Terbeche. "MINIMAX SHRINKAGE ESTIMATORS AND ESTIMATORS DOMINATING THE JAMES-STEIN ESTIMATOR UNDER THE BALANCED LOSS FUNCTION." Eurasian Mathematical Journal 13, no. 2 (2022): 18–36. http://dx.doi.org/10.32523/2077-9879-2022-13-2-18-36.
Full text
2
Abonazel, Mohamed. "Bias correction methods for dynamic panel data models with fixed effects." International Journal of Applied Mathematical Research 6, no. 2 (2017): 58. http://dx.doi.org/10.14419/ijamr.v6i2.7774.
Full textAbstract:
This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.
3
Iqbal, Kanwal, Syed Muhammad Muslim Raza, Tahir Mahmood, and Muhammad Riaz. "Exploring mixture estimators in stratified random sampling." PLOS ONE 19, no. 9 (2024): e0307607. http://dx.doi.org/10.1371/journal.pone.0307607.
Full textAbstract:
Advancements in sensor technology have brought a revolution in data generation. Therefore, the study variable and several linearly related auxiliary variables are recorded due to cost-effectiveness and ease of recording. These auxiliary variables are commonly observed as quantitative and qualitative (attributes) variables and are jointly used to estimate the study variable’s population mean using a mixture estimator. For this purpose, this work proposes a family of generalized mixture estimators under stratified sampling to increase efficiency under symmetrical and asymmetrical distributions and study the estimator’s behavior for different sample sizes for its convergence to the Normal distribution. It is found that the proposed estimator estimates the population mean of the study variable with more precision than the competitor estimators under Normal, Uniform, Weibull, and Gamma distributions. It is also revealed that the proposed estimator follows the Cauchy distribution when the sample size is less than 35; otherwise, it converges to normality. Furthermore, the implementation of two real-life datasets related to the health and finance sectors is also presented to support the proposed estimator’s significance.
4
Cahalan, Jennifer A., Jason Gasper, and Jennifer Mondragon. "Catch estimation in the federal trawl fisheries off Alaska: a simulation approach to compare the statistical properties of three trip-specific catch estimators." Canadian Journal of Fisheries and Aquatic Sciences 72, no. 7 (2015): 1024–36. http://dx.doi.org/10.1139/cjfas-2014-0347.
Full textAbstract:
Quantifying catch has been recognized worldwide as a critical component in fisheries management. Assessment of discard is challenging because of the requirement for at-sea observation, which is both logistically difficult and costly to fishery agencies. Statistical estimators using robust sampling methods may yield accurate and imprecise estimates given the variability associated with many at-sea discard species and inability for agencies to obtain high sampling fractions. However, biased estimates occur if an inappropriate estimator is used. Using Alaska trawl fisheries as an example, we investigated the statistical properties and implementation issues for three commonly used estimators: the ratio estimator; a simple mean estimator; and a deterministic imputation method currently in use in federal fisheries off Alaska. We used a simulation approach to evaluate the performance of these estimators to estimate trip-specific catch. Several statistical properties were evaluated: bias of the estimators, variability of the estimators, and accuracy of the variance estimators. The simple mean estimator had the best performance for vessels landing catch at shoreside processors. The choice of estimator was less clear for vessels processing catch, owing to sensitivity associated with species composition and implementation issues for the simple mean and ratio estimators.
5
Hamdaoui, Abdenour, Waleed Almutiry, Mekki Terbeche, and Abdelkader Benkhaled. "Comparison of Risk Ratios of Shrinkage Estimators in High Dimensions." Mathematics 10, no. 1 (2021): 52. http://dx.doi.org/10.3390/math10010052.
Full textAbstract:
In this paper, we analyze the risk ratios of several shrinkage estimators using a balanced loss function. The James–Stein estimator is one of a group of shrinkage estimators that has been proposed in the existing literature. For these estimators, sufficient criteria for minimaxity have been established, and the James–Stein estimator’s minimaxity has been derived. We demonstrate that the James–Stein estimator’s minimaxity is still valid even when the parameter space has infinite dimension. It is shown that the positive-part version of the James–Stein estimator is substantially superior to the James–Stein estimator, and we address the asymptotic behavior of their risk ratios to the maximum likelihood estimator (MLE) when the dimensions of the parameter space are infinite. Finally, a simulation study is carried out to verify the performance evaluation of the considered estimators.
6
Sharma, Rubal, and Sangeeta Malik. "Exponential Type Ratio and Product Estimator of Finite Population Mean in Stratified Sampling." Indian Journal Of Science And Technology 17, no. 40 (2024): 4168–76. http://dx.doi.org/10.17485/ijst/v17i40.2237.
Full textAbstract:
Objective: This article presents an exponential-type ratio and product estimator of the finite population mean within the stratification framework. A common technique in survey sampling is stratification, which divides the population into similar subsets to improve the estimator's accuracy. Methods: This paper tackles the problem of determining the expression for bias and Mean Square Error (MSE) for the population up to the 1st degree of the approximation when stratification is present. Findings: Here it is obtained the optimum value of the modified estimator. The modified estimator is more effective than the unbiased, ratio and product type estimators. We have used one data set through the stratified random sampling technique, and the relative efficiency of the estimators obtained from the population set is higher. Novelty: New proposed stratification estimators that improve the existing mixed ratio and product exponential types. The primary goal of this study is to investigate how well the suggested estimators perform compared to current estimators. Thorough simulation research to test the estimator's accuracy and efficiency. Both theoretical and practical research have shown that the proposed estimators outperform competing estimators. Keywords: Finite population mean, Stratification, Bias, Mean square error, Percent relative efficiency
7
Audu, A., S. A. Abdulazeez, I. Abubakar, Y. M. Ahijjo, A. Gidado, and M. A. Yunusa. "Modified Classes of Regression-Type Estimators of Population Mean in the Presence of Auxiliary Attribute under Double Sampling Scheme." Nigerian Journal of Basic and Applied Sciences 30, no. 2 (2023): 42–53. http://dx.doi.org/10.4314/njbas.v30i2.6.
Full textAbstract:
In sample survey, reliable and efficient estimates are often obtained using information from auxiliary variables during estimation and designing stages. However, there are times when the auxiliary information is attribute-based. Some authors have proposed estimators using auxiliary attribute information when the population mean of auxiliary attribute is unknown. However, the estimators are ratio- based estimators which are less efficient when the bi-serial correlation between the study variable and the auxiliary attribute is negative. In this study, regression approach was used to modify estimator d ZKi t to produce estimators that can be used for both negative and positive correlation. In addition, another existing estimator was also modified to produce an estimator that is independent of an unknown population parameter. The Biases and Mean Squared Errors (MSEs) of the modified estimators were determined using the Taylor series approach up to the first order of approximation. The proposed estimators’ efficiency conditions over some existing estimators were established. Empirical investigations were done using stimulation study and the results revealed that proposed estimators have the lowest MSEs and the highest PREs of all the competing estimators and therefore can give better estimates of the population mean. Therefore, it can be concluded that proposed estimators have better predictive power for estimating population mean under two-phase sampling scheme.
8
Hinrichsen, Richard A. "The accuracy of alternative stochastic growth rate estimates for salmon populations." Canadian Journal of Fisheries and Aquatic Sciences 59, no. 6 (2002): 1014–23. http://dx.doi.org/10.1139/f02-065.
Full textAbstract:
The accuracies of four alternative estimators of stochastic growth rate for salmon populations are examined using bootstrapping. The first estimator is based on a stochastic Leslie matrix model that uses age-specific spawner counts. The other three estimators use spawner counts with limited age-structure information: a BotsfordBrittnacher model method and two diffusion approximation methods, namely, the least squares approach of Dennis and the robust approach of Holmes. Accuracy of the estimators was quantified using median bias and interquartile ranges of the stochastic growth rate estimates. The BotsfordBrittnacher estimator was found to be unreliable due to large bias. Of the remaining estimators, the stochastic Leslie approach tended to produce the most reliable estimates but had the greatest data demands. With severe lognormal measurement error, the Dennis estimators produced less biased estimates than the other methods, but precision of the stochastic growth rate was generally highest using the stochastic Leslie estimator.
9
Rubal, Sharma, and Malik Sangeeta. "Exponential Type Ratio and Product Estimator of Finite Population Mean in Stratified Sampling." Indian Journal of Science and Technology 17, no. 40 (2024): 4168–76. https://doi.org/10.17485/IJST/v17i40.2237.
Full textAbstract:
Abstract <strong>Objective:</strong> This article presents an exponential-type ratio and product estimator of the finite population mean within the stratification framework. A common technique in survey sampling is stratification, which divides the population into similar subsets to improve the estimator's accuracy. <strong>Methods:</strong> This paper tackles the problem of determining the expression for bias and Mean Square Error (MSE) for the population up to the 1st degree of the approximation when stratification is present.<strong> Findings:</strong> Here it is obtained the optimum value of the modified estimator. The modified estimator is more effective than the unbiased, ratio and product type estimators. We have used one data set through the stratified random sampling technique, and the relative efficiency of the estimators obtained from the population set is higher. <strong>Novelty:</strong> New proposed stratification estimators that improve the existing mixed ratio and product exponential types. The primary goal of this study is to investigate how well the suggested estimators perform compared to current estimators. Thorough simulation research to test the estimator's accuracy and efficiency. Both theoretical and practical research have shown that the proposed estimators outperform competing estimators. <strong>Keywords:</strong> Finite population mean, Stratification, Bias, Mean square error, Percent relative efficiency
10
M. Elgohary, Mervat, Mohamed R. Abonazel, Nahed M. Helmy, and Abeer R. Azazy. "New robust-ridge estimators for partially linear model." International Journal of Applied Mathematical Research 8, no. 2 (2019): 46. http://dx.doi.org/10.14419/ijamr.v8i2.29932.
Full textAbstract:
This paper considers the partially linear model when the explanatory variables are highly correlated as well as the dataset contains outliers. We propose new robust biased estimators for this model under these conditions. The proposed estimators combine least trimmed squares and ridge estimations, based on the spline partial residuals technique. The performance of the proposed estimators and the Speckman-spline estimator has been examined by a Monte Carlo simulation study. The results indicated that the proposed estimators are more efficient and reliable than the Speckman-spline estimator.
11
Schreuder, H. T., H. G. Li, and J. W. Hazard. "PPS and Random Sampling Estimation Using some Regression and Ratio Estimators for Underlying Linear and Curvilinear Models." Forest Science 33, no. 4 (1987): 997–1009. http://dx.doi.org/10.1093/forestscience/33.4.997.
Full textAbstract:
Abstract Two thousand samples of 30 units were drawn from selected populations for which linear or curvilinear underlying models were postulated between the variable of interest and a covariate. Ratio, and linear and nonlinear regression estimators were compared for bias and relative efficiency of the estimates generated. Regression estimators were found to be the most precise estimators of totals for both random and probability proportional to size (PPS) sampling for a series of tree populations for samples of size 30. The weighted regression estimator in PPS sampling was consistently more efficient than the standard Horvitz-Thompson estimator. For the populations studied, the nonlinear and polynomial regression estimators were not efficient except in very specific cases, probably due to the absence of clear nonlinear trends in most of the populations. (Such nonlinear or curvilinear models do exist in specific stands for certain variables.) The quadratic polynomial regression estimator had the smallest variance in the case where a clear nonlinear relationship existed in the population for the variable pair considered. A general nonlinear regression estimator was inefficient for a population with a nonlinear relationship. Generally, estimation bias was small and coverage probabilities (containing the parameter of interest) were high for all estimators and populations. Jackknife variance estimates were not consistently better than the classical variance estimates of the true variances for any of the estimators. For. Sci. 33(4):997-1009.
12
Payandeh, Bijan, and Alan R. Ek. "Distance methods and density estimators." Canadian Journal of Forest Research 16, no. 5 (1986): 918–24. http://dx.doi.org/10.1139/x86-163.
Full textAbstract:
The relative performance of five distance–density estimators was evaluated for the n-tree circular, semicircular, and strip plots on several simulated and natural tree populations. Results indicate that for the n-tree circular plot, the ratio estimator performed very well for most populations examined and for n > 10. The performance of both the maximum likelihood and first moment estimators was affected to a great degree by the spatial pattern of the populations, but they performed satisfactorily for the random and uniform populations and for large n values (i.e., n > 10). Smaltschinski's estimator resulted in nearly bias-free estimates for nonaggregated populations and for all n, but performed poorly otherwise. The generalized Prodan estimator performed well for the random population, but overestimated the density otherwise. The relative performance of all estimators for the n-tree semicircular plot was quite similar to that of estimators for the n-tree circular plot, except that the former tended to produce lower density estimates. For n-tree strip plots, the generalized Prodan and the ratio estimator performed very well for the nonaggregated populations and for n > 10. All other estimators resulted in density estimates lower than those for n-tree circular and semicircular plots.
13
Hassan, Yasir, Muhammad Farooq, Saleha Yasir, and Will Murray. "On Some New Exponential Ratio Estimator of Population Mean in Two Phase Sampling." Sains Malaysiana 52, no. 7 (2023): 2149–62. http://dx.doi.org/10.17576/jsm-2023-5207-20.
Full textAbstract:
In this paper, we suggest employing the exponential ratio estimator to estimate the mean of the study variable using a two-phase sample strategy with two modified auxiliary variables. Several researchers discussed the properties of the estimators they proposed and discovered that the estimators in their studies were relatively efficient. The estimators previously studied are listed chronologically in the appendix to this paper. In two phase sampling, the estimator's mean square errors and relative efficiencies are calculated using auxiliary variable information. To assess the properties of our proposed estimator, we noticed that it has a lower mean square error (MSE) than the classical ratio estimator and some other exponential ratio estimators. The estimator is more useful than other estimators in solving real-world issues, notably in engineering, environmental science, management, and biological sciences. The proposed estimator has been applied to real-world data sets such as BRICS, Son's Head Measurement, Number of Hospital Beds, Sale Price of Residence, Ambient Pressure (AP), and Heating Load. In survey research, our suggested estimator has also been demonstrated to be more effective.
14
YADAV, SUBHASH KUMAR, DIKSHA ARYA, TUBA KOC, and TOLGA ZAMAN. "AN EFFICIENT FAMILY OF RATIO TYPE ESTIMATORS FOR SIMPLE RANDOM SAMPLING." Journal of Science and Arts 24, no. 1 (2024): 69–94. http://dx.doi.org/10.46939/j.sci.arts-24.1-a07.
Full textAbstract:
The study provides an enhanced estimation of the population mean using known information on an auxiliary variable. An enhanced class of estimators is suggested for the same. The proposed estimator's bias and mean squared error (MSE) are calculated up to the first order of approximation. The optimum values of the characterizing constants are obtained by minimizing the MSE of the proposed estimator. The minimum MSE and the bias values are achieved by optimising the characterizing scalar. The MSE of the proposed estimator has also been compared both conceptually and empirically with the MSEs of competing estimators. Real and simulated data sets are adopted to verify the theoretical prerequisites for the proposed estimator's greater efficiency over competing estimators. The most efficient estimator is recommended for practical utility in different areas of applications and the suggested estimator filfills the requirement.
15
Zaagan, Abdullah A., Mutum Zico Meetei, Shreyanshu Singh, Rajesh Gupta, Subhash Kumar Yadav, and Mukesh Kumar Verma. "Computing the Population Mean in Stratified Sampling Using an Auxiliary Attribute." Journal of Autonomous Intelligence 7, no. 5 (2024): 1672. http://dx.doi.org/10.32629/jai.v7i5.1672.
Full textAbstract:
<p>This paper proposes a novel ratio type estimator for the population mean using an auxiliary attribute by implying one auxiliary variable in stratified random sampling using conventional product, exponential, and logarithmic ratio type estimators. The proposed estimator’s MSE and PRE are determined, and PRE is compared with existing estimators. The proposed estimator is more effective than other existing estimators according to theoretical observations, which verifies its numerical examples. The proposed estimator may be used for practical applicability in real life, including agricultural sciences, biological sciences, commercial sciences, economic sciences, engineering sciences, medical sciences, social sciences etc.</p>
16
K, Oguagbaka, S., Okoli, O. C, and Aronu, C. O. "Ratio estimator for double sampling procedure with non-response: An empirical study." International Journal of Basic and Applied Science 12, no. 4 (2024): 148–58. http://dx.doi.org/10.35335/ijobas.v12i4.281.
Full textAbstract:
This study proposes a ratio-type estimator for population mean estimation using auxiliary variables with double sampling in the presence of non-response. The study provides expressions for the constant, bias, and mean square errors (MSE) of the proposed estimator and compares it with ten existing estimators. The study employed the secondary source of data collection to evaluate the efficiency of the proposed and existing estimators by analyzing five natural populations from three different sources. The performance of ten (10) estimators was considered in this study. The findings suggest that the proposed estimator and the H estimator provide more accurate and precise estimates of the population mean using an auxiliary variable. Additionally, the study found significant differences amongst the mean values of the constant and bias for the different estimators. A Dunn Kruskal-Wallis multiple comparison tests with the Bonferroni method was performed to ascertain the pair of estimators that contributed to the significant difference observed. When estimating the population means using an auxiliary variable, the proposed estimator outperformed other existing estimators that were taken into consideration in the study
17
Uma, Srivastava, and Kumar Harish. "Estimation of Cut Point in Burr III Sequence under Linear Exponential Loss." International Journal of Innovative Science and Research Technology 7, no. 7 (2022): 501–8. https://doi.org/10.5281/zenodo.6956269.
Full textAbstract:
This paper estimates the single cut-point in the mean of a Burr III Sequence and its scale parameters before and after the cut point. We introduce a strong estimator of the parameters with the help of Bayesian inference approach, by persevering these estimatorsin the criteria used to estimate the cut-point under Linear Exponential Loss Function. The simulation technique is used compare the estimators. Open-source R software is used in the simulation section. We have taken real data to estimate the parameters of the sequence and then hypothetical observations of the sequence to prove their robustness of the estimators.
18
Khan, Dost Muhammad, Muhammad Ali, Zubair Ahmad, Sadaf Manzoor, and Sundus Hussain. "A New Efficient Redescending M-Estimator for Robust Fitting of Linear Regression Models in the Presence of Outliers." Mathematical Problems in Engineering 2021 (November 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/3090537.
Full textAbstract:
Robust regression is an important iterative procedure that seeks analyzing data sets that are contaminated with outliers and unusual observations and reducing their impact over regression coefficients. Robust estimation methods have been introduced to deal with the problem of outliers and provide efficient and stable estimates in their presence. Various robust estimators have been developed in the literature to restrict the unbounded influence of the outliers or leverage points on the model estimates. Here, a new redescending M-estimator is proposed using a novel objective function with the prime focus on getting highly robust and efficient estimates that give promising results. It is evident from the results that, for normal and clean data, the proposed estimator is almost as efficient as ordinary least square method and, however, becomes highly resistant to outliers when it is used for contaminated datasets. The simulation study is being carried out to assess the performance of the proposed redescending M-estimator over different data generation scenarios including normal, t-distribution, and double exponential distributions with different levels of outliers’ contamination, and the results are compared with the existing redescending M-estimators, e.g., Huber, Tukey Biweight, Hampel, and Andrew-Sign function. The performance of the proposed estimators was also checked using real-life data applications of the estimators and found that the proposed estimators give promising results as compared to the existing estimators.
19
Lady, James M., and John R. Skalski. "Estimators of stream residence time of Pacific salmon (Oncorhynchus spp.) based on release-recapture data." Canadian Journal of Fisheries and Aquatic Sciences 55, no. 12 (1998): 2580–87. http://dx.doi.org/10.1139/f98-132.
Full textAbstract:
The area-under-the-curve method is a widely used method for estimating salmon escapement. The method depends on obtaining an accurate estimate of stream residence time, or stream life. This paper develops two estimators of stream residence time based on release-recapture data: a nonparametric estimator and a parametric estimator. Monte Carlo simulations showed that with an adequate release size and number of sampling occasions, both estimators provide precise estimates of stream residence time. If there is significant right censoring, however, the parametric estimator is significantly less biased. If the data are too sparse, the parametric estimator performs poorly and often fails. The stream residence time of spawning sockeye salmon (Oncorhynchus nerka) in Iliamna Lake, Alaska, was estimated using the estimators developed here. Because the estimators also provide the variance of the estimates, the precision of the stream residence time estimate could be assessed, and we were able to test and reject the hypothesis that the stream residence time for females is equal to that of males. Both estimators are applicable to estimating the life expectancy of any fish or wildlife population with release-recapture techniques.
20
Malik, Sachin, and Rajesh Singh. "Some Improved Multivariate-Ratio-Type Estimators Using Geometric and Harmonic Means in Stratified Random Sampling." ISRN Probability and Statistics 2012 (August 26, 2012): 1–7. http://dx.doi.org/10.5402/2012/509186.
Full textAbstract:
Auxiliary variable is commonly used in survey sampling to improve the precision of estimates. Whenever there is auxiliary information available, we want to utilize it in the method of estimation to obtain the most efficient estimator. In this paper using multiauxiliary information we have proposed estimators based on geometric and harmonic mean. It was also shown that estimators based on harmonic mean and geometric mean are less biased than Olkin (1958) and Singh (1967) estimators under certain conditions. However, the MSE of Olkin (1958) estimator and geometric and harmonic estimators are same up to the first order of approximations.
21
Bhat, S. S., and R. Vidya. "Performance of Ridge Estimators Based on Weighted Geometric Mean and Harmonic Mean." Journal of Scientific Research 12, no. 1 (2020): 1–13. http://dx.doi.org/10.3329/jsr.v12i1.40525.
Full textAbstract:
Ordinary least squares estimator (OLS) becomes unstable if there is a linear dependence between any two predictors. When such situation arises ridge estimator will yield more stable estimates to the regression coefficients than OLS estimator. Here we suggest two modified ridge estimators based on weights, where weights being the first two largest eigen values. We compare their MSE with some of the existing ridge estimators which are defined in the literature. Performance of the suggested estimators is evaluated empirically for a wide range of degree of multicollinearity. Simulation study indicates that the performance of the suggested estimators is slightly better and more stable with respect to degree of multicollinearity, sample size, and error variance.
22
Yunusa, Mojeed Abiodun, Jamiu Olasunkanmi Muili, Ahmed Audu, and Ran Vijay Kumar Singh. "A Sine Type Median Based Estimator for the Estimation of Population Mean." Oriental Journal of Physical Sciences 8, no. 1 (2023): 21–26. http://dx.doi.org/10.13005/ojps08.01.05.
Full textAbstract:
In the literature, there are numerous estimators for estimating population means when auxiliary information is provided. Subramani suggested ratio median based estimator when the median of the study variable is available and the regression estimator was shown to be significantly less efficient than the estimator. In this research, we suggested an estimator for the population mean of the studied variable based on a sine type median. Using Taylor series expansion, the bias and mean square error of the estimator were obtained up to the first order of approximation. The condition under which the proposed estimator is more efficient than the existing estimators was established. An empirical investigation was done to compare the suggested estimator's efficiency to that of the existing estimators, and the numerical findings showed that the proposed estimator is more efficient.
23
Pal, Surya K., Sagir A. Mahmud, Madan M. Gupta, Housila P. Singh, and Ramkrishna S. Solanki. "Estimation of finite population mean in sample surveys: A new estimator." Journal of Information & Optimization Sciences 44, no. 1 (2023): 157–69. http://dx.doi.org/10.47974/jios-1304.
Full textAbstract:
Utilizing supplementary information in simple random sampling, this research paper discussed a new method for finding the finite population mean of a predictive variable, and the properties of the suggested method have been investigated. The suggested estimator’s advantages over traditional estimators are demonstrated using theoretical asymptotic techniques and empirical analysis. The recommended estimator outperforms the customary unbiased, ratio, product, and regression estimators, as well as many other known population mean estimators.
24
Hone, J. "A Test of the Accuracy of Line and Strip Transect Estimators in Aerial Survey." Wildlife Research 15, no. 5 (1988): 493. http://dx.doi.org/10.1071/wr9880493.
Full textAbstract:
The accuracy and precision of eight line transect estimators and one strip transect estimator were examined by helicopter aerial survey. Carcasses of feral pigs were counted in an area of treeless floodplain and Eucalyptus woodland. The ratio and Cox's methods, Fourier series, exponential power series, half-normal, exponential polynomial, negative exponential, hermite polynomial and hazard rate estimators gave accurate estimates. Using the survey method described, most estimators were of similar accuracy and precision, but the Fourier series estimator was the most accurate.
25
Shaheen, Nazia, Muhammad Nouman Qureshi, Osama Abdulaziz Alamri, and Muhammad Hanif. "Optimized inferences of finite population mean using robust parameters in systematic sampling." PLOS ONE 18, no. 1 (2023): e0278619. http://dx.doi.org/10.1371/journal.pone.0278619.
Full textAbstract:
In this article, we have proposed a generalized estimator for mean estimation by combining the ratio and regression methods of estimation in the presence of auxiliary information using systematic sampling. We incorporated some robust parameters of the auxiliary variable to obtain precise estimates of the proposed estimator. The mathematical expressions for bias and mean square error of proposed the estimator are derived under large sample approximation. Many other generalized ratio and product-type estimators are obtained from the proposed estimator using different choices of scalar constants. Some special cases are also discussed in which the proposed generalized estimator reduces to the usual mean, classical ratio, product, and regression type estimators. Mathematical conditions are obtained for which the proposed estimator will perform more precisely than the challenging estimators mentioned in this article. The efficiency of the proposed estimator is evaluated using four populations. Results showed that the proposed estimator is efficient and useful for survey sampling in comparison to the other existing estimators.
26
Little, Roderick J., and Roger J. Lewis. "Estimands, Estimators, and Estimates." JAMA 326, no. 10 (2021): 967. http://dx.doi.org/10.1001/jama.2021.2886.
Full text
27
UBA, T. "Hybrid Exponential Type Estimators of Finite Population Mean in Double Sampling." International Journal of Research and Innovation in Applied Science X, no. II (2025): 453–72. https://doi.org/10.51584/ijrias.2025.10020041.
Full textAbstract:
In this study, two-auxiliary exponential type estimators of finite population mean in Two-phase are proposed. The proposed estimators are extension of (1) estimator finite population mean in SRS to Two-phase sampling. The study investigated efficiency of the proposed estimators by utilizing the ratio of bias to standard error (RBSE) as a proxy to examine confidence limits for estimates. The expressions for the bias and Mean Square Error (MSE) of the estimators were derived. A comprehensive simulation study was carried out to show the efficacy of the estimators as compared to conventional estimators using Coefficient of Variation as a performance measure. Furthermore, a small sample from real data set was utilized to validate the performance of proposed estimators under two varying correlation coefficients amongst variables in the parameter space. The results of both the simulation study and real life studies have shown that the proposed estimators were not only asymptotic, more efficient but produces estimates that are more precise than most of the existing estimators considered in this study.
28
Mohammadzaheri, Morteza, Mohammadreza Emadi, Mojtaba Ghodsi, Issam M. Bahadur, Musaab Zarog, and Ashraf Saleem. "Development of a Charge Estimator for Piezoelectric Actuators." International Journal of Artificial Intelligence and Machine Learning 10, no. 1 (2020): 31–44. http://dx.doi.org/10.4018/ijaiml.2020010103.
Full textAbstract:
Charge of a piezoelectric actuator is proportional to its displacement for a wide area of operating. Hence, a charge estimator can estimate displacement for such actuators. However, existing charge estimators take a sizable portion of the excitation voltage, i.e. voltage drop. Digital charge estimators have presented the smallest voltage drop. This article first investigates digital charge estimators and suggests a design guideline to (i) maximise accuracy and (ii) minimise the voltage drop. Digital charge estimators have a sensing resistor; an estimator with a constant resistance is shown to violate the design guideline; while, all existing digital charge estimators use one or a few intuitively chosen resistors. That is, existing estimators witness unnecessarily large inaccuracy and/or voltage drop. This research develops charge estimators with varying resistors, fulfilling the design guideline. Several methods are tested to estimates the sensing resistance based on operating conditions, and radial basis function networks models excel in terms of accuracy.
29
Beauducel, André, Christopher Harms, and Norbert Hilger. "Reliability Estimates for Three Factor Score Estimators." International Journal of Statistics and Probability 5, no. 6 (2016): 94. http://dx.doi.org/10.5539/ijsp.v5n6p94.
Full textAbstract:
Estimates for the reliability of Thurstone’s regression factor score estimator, Bartlett’s factor score estimator, and McDonald’s factor score estimator were proposed. Moreover, conditions for equal reliability of the factor score estimators were presented and the reliability estimates were compared by means of simulation studies. Under conditions inducing unequal reliabilities, reliability estimates were largest for the regression score estimator and lowest for McDonald’s factor score estimator. We provide an R-script and an SPSS-script for the computation of the respective reliability estimates.
30
O. J, Oladapo, Idowu J. I., Owolabi A. T., Ayinde K., and Adejumo T. J. "A New Ridge Type Estimator in the Logistic Regression Model with Correlated Regressors." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 21 (December 20, 2024): 2528–41. https://doi.org/10.37394/23207.2024.21.208.
Full textAbstract:
The maximum likelihood (ML) technique is always one of the most widely employed to estimate model parameters in logistic regression models. However, due to the problem of multicollinearity, unstable parameter estimates, and inaccurate variance which affects confidence intervals and hypothesis tests can be achieved. A new two-parameter biased estimator is proposed in this paper to handle multicollinearity in binary logistic regression models. The proposed estimator's properties were determined, and five (5) different types of biasing parameter k (generalized, maximum, median, mid-range, and arithmetic mean) were applied in this work. The necessary and sufficient criteria for the new two-parameter biased estimators to outperform the existing estimators is considered. In addition, Monte Carlo simulation studies are carried out to compare the performance of the proposed biased estimator. Finally, a numerical example is provided to support the theoretical and simulations findings.
31
Hidiroglou, M. A., and V. M. Estevao. "A comparison of small area and calibration estimators via simulation." Statistics in Transition new series 17, no. 1 (2016): 133–54. http://dx.doi.org/10.59170/stattrans-2016-007.
Full textAbstract:
Domain estimates are typically obtained using calibration estimators that are direct or modified direct. They are direct if they strictly use data within the domain of interest. They are modified direct if they use both data within and outside the domain of interest. An alternative way of producing these estimates is through small area procedures. In this article, we compare the performance of these two approaches via a simulation. The population is generated using a hierarchical model that includes both area effects and unit level random errors. The population is made up of mutually exclusive domains of different sizes, ranging from a small number of units to a large number of units. We select many independent simple random samples of fixed size from the population and compute various estimates for each sample using the available auxiliary information. The estimates computed for the simulation included the Horvitz-Thompson estimator, the synthetic estimator (indirect estimate), calibration estimators, and unit level based estimators (small area estimate). The performance of these estimators is summarized based on their design- based properties.
32
Singh, G. N., and Kumari Priyanka. "Estimation of population mean at current occasion in presence of several varying auxiliary variates in two-occasion successive sampling." Statistics in Transition new series 11, no. 1 (2010): 105–26. http://dx.doi.org/10.59170/stattrans-2010-007.
Full textAbstract:
The present work intended to emphasize the role of several varying auxiliary variates at both the occasions to improve the precision of estimates at current occasion in two-occasion successive sampling. Two different efficient estimators are proposed and their theoretical properties are examined. Relative comparison of efficiencies of the proposed estimators with the sample mean estimator when there is no matching from previous occasion, and the optimum successive sampling estimator when no auxiliary information is used have been incorporated. Empirical studies are significantly justifying the composition of proposed estimators.
33
Al-Omari, Amer Ibrahim, SidAhmed Benchiha, and Ibrahim M. Almanjahie. "Efficient Estimation of Two-Parameter Xgamma Distribution Parameters Using Ranked Set Sampling Design." Mathematics 10, no. 17 (2022): 3170. http://dx.doi.org/10.3390/math10173170.
Full textAbstract:
An efficient method such as ranked set sampling is used for estimating the population parameters when the actual observation measurement is expensive and complicated. In this paper, we consider the problem of estimating the two-parameter xgamma (TPXG) distribution parameters under the ranked set sampling as well as the simple random sampling design. Various estimation methods, including the weighted least-square estimator, maximum likelihood estimators, least-square estimator, Cramer–von Mises, the maximum product of spacings estimators, and Anderson–Darling estimators, are considered. A comparison between the ranked set sampling and simple random sampling estimators, with the same number of measurement units, is conducted using a simulation study in terms of the bias, mean squared errors, and efficiency of estimators. The merit of the ranked set sampling estimators is examined using real data of bank customers. The results indicate that estimations using the ranked set sampling method are more efficient than the simple random sampling competitor considered in this study.
34
Attah, Omokova M., and Samuel Olayemi Olanrewaju. "Efficient Combined Estimator for Parameter Estimation of Linear Regression Model with Multicollinearity." Asian Journal of Probability and Statistics 27, no. 5 (2025): 1–11. https://doi.org/10.9734/ajpas/2025/v27i5750.
Full textAbstract:
The classical linear regression model relies on several key assumptions, including homoscedasticity, normality of errors, independence of observations and the absence of multicollinearity among explanatory variables (Gujarati, 2021), These assumptions are rarely fulfilled in real life situations. Multicollinearity occurs when the assumption of independent explanatory variables is violated (Alreshidi et al., 2025), There are many sources of multicollinearity, some of which are the data collection methods, the constraints placed on the model or having an overdetermined model (Paul, 2006), When multicollinearity exists in a model using conventional parameter estimation models like the Ordinary Least Squares (OLS) often leads to unstable and unreliable parameter estimates. (Alharthi & Akhtar, 2025), To address these challenges several biased estimators have been developed. Some of these are the ridge (Hoerl and Kennard, 1970), liu (Liu, 1993), and principal components (pc) (Hotelling, 1933), estimators. Each of these existing estimators have their strengths and limitations. However, no single estimator consistently outperforms the others under all conditions. Over the years other researchers have developed combined estimators with the expectation that the combination of different estimators might inherit the individual advantages of the estimators. Following their work, in this paper effort is made to provide a combined estimator based on ridge (Hoerl and Kennard, 1970), liu (Liu, 1993), and principal components (pc) (Hotelling, 1933), estimators. This estimator- the principal component ridge liu, leverages on the strengths of these three existing estimators. The performance of the developed estimator is compared with the existing individual estimators using Mean Square Error (MSE). Results show that the developed estimator performs better than the existing ones providing more stable and accurate parameter estimates in the presence of multicollinearity. Monte Carlo experiments a robust method for assessing statistical properties under controlled conditions with varying degrees of multicollinearity, error variance, and sample size (Oduntan, 2024), were performed one thousand (1000) times on two (2) linear regression models with four (4) and seven (7) explanatory variables exhibiting five (5) degrees of multicollinearity (0.75, 0.85, 0.95, 0.99, 0.999), three(3) levels of error variance (1, 25, 100), at eight (8) sample sizes (n=10, 15, 20,30,40,50,100 and 250). The MSE criterion was used to examine the estimators and the number of times each estimator had the minimum MSE was counted at each combination of classifications. The ranking of the estimators was also done based on their MSE. Tables and figures were used to present the results of the findings. The results of the investigation revealed that when multicollinearity problems exist in linear regression models, the proposed RHKMALUMIWPC estimator is best.
35
Chadyšas, V., and D. Krapavickaitė. "Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling." Nonlinear Analysis: Modelling and Control 10, no. 4 (2005): 333–42. http://dx.doi.org/10.15388/na.2005.10.4.15113.
Full textAbstract:
Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.
36
Polacheck, Tom, Ray Hilborn, and Andre E. Punt. "Fitting Surplus Production Models: Comparing Methods and Measuring Uncertainty." Canadian Journal of Fisheries and Aquatic Sciences 50, no. 12 (1993): 2597–607. http://dx.doi.org/10.1139/f93-284.
Full textAbstract:
Three approaches are commonly used to fit surplus production models to observed data: effort-averaging methods; process-error estimators; and observation-error estimators. We compare these approaches using real and simulated data sets, and conclude that they yield substantially different interpretations of productivity. Effort-averaging methods assume the stock is in equilibrium relative to the recent effort; this assumption is rarely satisfied and usually leads to overestimation of potential yield and optimum effort. Effort-averaging methods will almost always produce what appears to be "reasonable" estimates of maximum sustainable yield and optimum effort, and the r2 statistic used to evaluate the goodness of fit can provide an unrealistic illusion of confidence about the parameter estimates obtained. Process-error estimators produce much less reliable estimates than observation-error estimators. The observation-error estimator provides the lowest estimates of maximum sustainable yield and optimum effort and is the least biased and the most precise (shown in Monte-Carlo trials). We suggest that observation-error estimators be used when fitting surplus production models, that effort-averaging methods be abandoned, and that process-error estimators should only be applied if simulation studies and practical experience suggest that they will be superior to observation-error estimators.
37
Muhammad, Isah. "Generalized Ratio-Product cum Regression Variance Estimator in Two-Phase Sampling." Central Bank of Nigeria Journal of Applied Statistics 14, no. 2 (2023): 73–101. http://dx.doi.org/10.33429/cjas.14223.4/5.
Full textAbstract:
This study develops a flexible and efficient generalized ratio-product cum regression-type estimator of population variance utilizing auxiliary variable in two-phase sampling that incorporates the properties of ratio-type and product-type estimators. The properties of the estimator were derived using first order approximation. The theoretical conditions under which the precision and the flexibility of the estimator is better than some classical estimators are also provided. Empirical evidence from five real datasets suggests that the proposed estimator outperforms the classical variance, ratio variance, product, and exponential ratio type estimators in terms of precision and efficiency. The estimator can be utilized to provide better variance estimates for various phenomena such as inflation variation, exchange rate variation and standard of living variation for better policymaking.
38
Krieg, Sabine, Harm Jan Boonstra, and Marc Smeets. "Small-Area Estimation with Zero-Inflated Data – a Simulation Study." Journal of Official Statistics 32, no. 4 (2016): 963–86. http://dx.doi.org/10.1515/jos-2016-0051.
Full textAbstract:
Abstract Many target variables in official statistics follow a semicontinuous distribution with a mixture of zeros and continuously distributed positive values. Such variables are called zero inflated. When reliable estimates for subpopulations with small sample sizes are required, model-based small-area estimators can be used, which improve the accuracy of the estimates by borrowing information from other subpopulations. In this article, three small-area estimators are investigated. The first estimator is the EBLUP, which can be considered the most common small-area estimator and is based on a linear mixed model that assumes normal distributions. Therefore, the EBLUP is model misspecified in the case of zero-inflated variables. The other two small-area estimators are based on a model that takes zero inflation explicitly into account. Both the Bayesian and the frequentist approach are considered. These small-area estimators are compared with each other and with design-based estimation in a simulation study with zero-inflated target variables. Both a simulation with artificial data and a simulation with real data from the Dutch Household Budget Survey are carried out. It is found that the small-area estimators improve the accuracy compared to the design-based estimator. The amount of improvement strongly depends on the properties of the population and the subpopulations of interest.
39
Shah, M. Younis, and S. E. H. Rizvi. "Improvement for Estimation of Population Mean in Post Stratification using Supplementary Variable." Journal of Scientific Research 67, no. 04 (2023): 47–51. http://dx.doi.org/10.37398/jsr.2023.670408.
Full textAbstract:
This investigation, considers the estimation for finite population mean utilizing knowledge from a supplementary variable in case of post stratification. We examine the suggested estimator's sample characteristics up to an approximation of order one. The ideal value of the constant in the suggested estimator has been found to have the lowest mean squared error (MSE). The suggested estimator is contrasted with alternative estimators. An empirical illustration verifies the theoretical results. We demonstrate the advantages of the suggested estimator over the competing estimators via an empirical example.
40
Li, H. G., H. T. Schreuder, and C. T. Scott. "Combining estimates that are both in error subject to marginal constraints." Canadian Journal of Forest Research 20, no. 10 (1990): 1675–79. http://dx.doi.org/10.1139/x90-221.
Full textAbstract:
Outside estimates with measures of reliability can be combined with existing tabular estimates of resource statistics to produce new, more precise estimates. But cell estimates do not sum to the original marginal or overall totals when this is done. A method is given to adjust the unchanged cell values to maintain additivity. Classical and bootstrap variance estimators are given for the n × 2 case of combining a cell proportion with an outside estimate assumed to be binomially distributed under fixed marginal constraints, and for the n × m case of combining a cell proportion with a binomially distributed outside estimate under no marginal constraints except that the table total is fixed. For a 3 × 2 test case, a bootstrap variance estimator yielded reliable estimates of precision for the adjusted cell proportions in most cases. For the n × m case, a classical variance estimator was more stable than the bootstrap variance estimators and was less biased than the other variance estimators studied.
41
Huitema, Bradley E., and Joseph W. McKean. "Two Reduced-Bias Autocorrelation Estimators: rF1 and rF2." Perceptual and Motor Skills 78, no. 1 (1994): 323–30. http://dx.doi.org/10.2466/pms.1994.78.1.323.
Full textAbstract:
Among the problems associated with the application of time-series analysis to typical psychological data are difficulties in parameter estimation. For example, estimates of autocorrelation coefficients are known to be biased in the small-sample case. Previous work by the present authors has shown that, in the case of conventional autocorrelation estimators of ρ1 the degree of bias is more severe than is predicted by formulas that are based on large-sample theory. Two new autocorrelation estimators, rF1 and rF2, were proposed; a Monte Carlo experiment was carried out to evaluate the properties of these statistics. The results demonstrate that both estimators provide major reduction of bias. The average absolute bias of rF2 is somewhat smaller than that of rF1 at all sample sizes, but both are far less biased than is the conventional estimator found in most time-series software. The reduction in bias comes at the price of an increase in error variance. A comparison of the properties of these estimators with those of other estimators suggested in 1991 shows advantages and disadvantages for each. It is recommended that the choice among autocorrelation estimators be based upon the nature of the application. The new estimator rF2 is especially appropriate when pooling estimates from several samples.
42
Newey, Whitney K. "Kernel Estimation of Partial Means and a General Variance Estimator." Econometric Theory 10, no. 2 (1994): 1–21. http://dx.doi.org/10.1017/s0266466600008409.
Full textAbstract:
Econometric applications of kernel estimators are proliferating, suggesting the need for convenient variance estimates and conditions for asymptotic normality. This paper develops a general “delta-method” variance estimator for functionals of kernel estimators. Also, regularity conditions for asymptotic normality are given, along with a guide to verify them for particular estimators. The general results are applied to partial means, which are averages of kernel estimators over some of their arguments with other arguments held fixed. Partial means have econometric applications, such as consumer surplus estimation, and are useful for estimation of additive nonparametric models.
43
Ijaz, Muhammad, Syed Muhammad Asim, Atta ullah, and Ibrahim Mahariq. "Flexible Robust Regression-Ratio Type Estimators and Its Applications." Mathematical Problems in Engineering 2022 (September 28, 2022): 1–6. http://dx.doi.org/10.1155/2022/8977392.
Full textAbstract:
In real-world situations, the data set under examination may contain uncommon noisy measurements that unreasonably affect the data’s outcome and produce incorrect model estimates. Practitioners employed robust-type estimators to reduce the weight of the noisy measurements in a data set in such a scenario. Using auxiliary information that will produce reliable estimates, we have looked at a few flexible robust-type estimators in this study. In order to estimate the population mean, this study presents unique flexible robust regression type ratio estimators that take into account the data from the midrange and interdecile range of the auxiliary variables. Up to the first order of approximate computation, the bias and mean square were calculated. In order to compare the flexibility of the proposed estimator to those of the existing estimators, theoretical conditions were also obtained. We took into account data sets containing outliers for empirical computation, and it was found that the suggested estimators produce results with higher precision than the existing estimators.
44
Chandni, Kumari, and Kumar Thakur Ratan. "On Construction of Modified Class of Estimators for Population Variance using Auxiliary Attribute." International Journal of Management and Humanities (IJMH) 4, no. 9 (2020): 109–19. https://doi.org/10.35940/ijmh.I0878.054920.
Full textAbstract:
In this paper, an improved estimator for population variance has been proposed to improvise the log-type estimators proposed by Kumari et al. (2019). The properties of proposed estimators are derived up to the first order of approximation. The proposed estimatorfound to be betterthan the existing estimatorsin the sense of mean squraed error and percent relative efficiency. A numerical study is included to support the use of the suggested classes of estimators.
45
Aladeitan, BENEDICTA, Adewale F. Lukman, Esther Davids, Ebele H. Oranye, and Golam B. M. Kibria. "Unbiased K-L estimator for the linear regression model." F1000Research 10 (August 19, 2021): 832. http://dx.doi.org/10.12688/f1000research.54990.1.
Full textAbstract:
Background: In the linear regression model, the ordinary least square (OLS) estimator performance drops when multicollinearity is present. According to the Gauss-Markov theorem, the estimator remains unbiased when there is multicollinearity, but the variance of its regression estimates become inflated. Estimators such as the ridge regression estimator and the K-L estimators were adopted as substitutes to the OLS estimator to overcome the problem of multicollinearity in the linear regression model. However, the estimators are biased, though they possess a smaller mean squared error when compared to the OLS estimator. Methods: In this study, we developed a new unbiased estimator using the K-L estimator and compared its performance with some existing estimators theoretically, simulation wise and by adopting real-life data. Results: Theoretically, the estimator even though unbiased also possesses a minimum variance when compared with other estimators. Results from simulation and real-life study showed that the new estimator produced smaller mean square error (MSE) and had the smallest mean square prediction error (MSPE). This further strengthened the findings of the theoretical comparison using both the MSE and the MSPE as criterion. Conclusions: By simulation and using a real-life application that focuses on modelling, the high heating values of proximate analysis was conducted to support the theoretical findings. This new method of estimation is recommended for parameter estimation with and without multicollinearity in a linear regression model.
46
Fischer, Christoph, and Joachim Saborowski. "Variance estimation for mean growth from successive double sampling for stratification." Canadian Journal of Forest Research 50, no. 12 (2020): 1405–11. http://dx.doi.org/10.1139/cjfr-2020-0058.
Full textAbstract:
Double sampling for stratification (2SS) is a sampling design that is widely used for forest inventories. We present the mathematical derivation of two appropriate variance estimators for mean growth from repeated 2SS with updated stratification on each measurement occasion. Both estimators account for substratification based on the transition of sampling units among the strata due to the updated allocation. For the first estimator, sizes of the substrata were estimated from the second-phase sample (sample plots), whereas the respective sizes in the second variance estimator relied on the larger first-phase sample. The estimators were empirically compared with a modified version of Cochran’s well-known 2SS variance estimator that ignores substratification. This was done by performing bootstrap resampling on data from two German forest districts. The major findings were as follows: (i) accounting for substratification, as implemented in both new estimators, has substantial impact in terms of significantly smaller variance estimates and bias compared with the estimator without substratification, and (ii) the second estimator with substrata sizes being estimated from the first-phase sample shows a smaller bias than the first estimator.
47
Gould, W. R., L. A. Stefanski, and K. H. Pollock. "Use of simulation–extrapolation estimation in catch–effort analyses." Canadian Journal of Fisheries and Aquatic Sciences 56, no. 7 (1999): 1234–40. http://dx.doi.org/10.1139/f99-052.
Full textAbstract:
All catch-effort estimation methods implicitly assume catch and effort are known quantities, whereas in many cases, they have been estimated and are subject to error. We evaluate the application of a simulation-based estimation procedure for measurement error models (J.R. Cook and L.A. Stefanski. 1994. J. Am. Stat. Assoc. 89: 1314-1328) in catch-effort studies. The technique involves a simulation component and an extrapolation step, hence the name SIMEX estimation. We describe SIMEX estimation in general terms and illustrate its use with applications to real and simulated catch and effort data. Correcting for measurement error with SIMEX estimation resulted in population size and catchability coefficient estimates that were substantially less than naive estimates, which ignored measurement errors in some cases. In a simulation of the procedure, we compared estimators from SIMEX with "naive" estimators that ignore measurement errors in catch and effort to determine the ability of SIMEX to produce bias-corrected estimates. The SIMEX estimators were less biased than the naive estimators but in some cases were also more variable. Despite the bias reduction, the SIMEX estimator had a larger mean squared error than the naive estimator for one of two artificial populations studied. However, our results suggest the SIMEX estimator may outperform the naive estimator in terms of bias and precision for larger populations.
48
Salih, Ahmed Maher, Zakariya Algamal, and Mundher Abdullah Khaleel. "A New Ridge-Type Estimator for the Gamma regression model." Iraqi Journal For Computer Science and Mathematics 5, no. 1 (2024): 85–98. http://dx.doi.org/10.52866/ijcsm.2024.05.01.006.
Full textAbstract:
When there is collinearity among the regressors in gamma regression models, we present a newtwo-parameter ridge estimator in this study. We look into the new estimator's mean squared error characteristics.Additionally, we offer several theorems to contrast the new estimators with the current ones. To compare theestimators under various collinearity designs in terms of mean squared error, we run a Monte Carlo simulationanalysis. We also offer a real data application to demonstrate the usefulness of the new estimator. The results fromsimulations and actual data reveal that the proposed estimator is superior to competing estimators.
49
Magnussen, Steen, and Thomas Nord-Larsen. "A Jackknife Estimator of Variance for a Random Tessellated Stratified Sampling Design." Forest Science 65, no. 5 (2019): 543–47. http://dx.doi.org/10.1093/forsci/fxy070.
Full textAbstract:
Abstract Semisystematic sampling designs—in which a population area frame is tessellated into cells, and a randomly located sample is taken from each cell—affords random tessellated stratified (RTS) Horvitz–Thompson-type estimators. Forest inventory applications with RTS estimators are rare, possibly because of computational complexities with the estimation of variance. To reduce this challenge, we propose a jackknife estimator of variance for RTS designs. We demonstrate an application with a model-assisted ratio of totals estimator and data from the Danish National Forest Inventory. RTS estimators of standard error were, as a rule, smaller than comparable estimates obtained under the assumption of simple random sampling. The proposed jackknife estimator performed well.
50
Onyango, Ronald, Samuel B. Apima, and Amos Wanjara. "Estimation of finite population mean of a sensitive variable using three-stage orrt in the presence of non-response and measurement errors." Engineering and Applied Science Letters 6, no. 1 (2023): 37–48. https://doi.org/10.30538/psrp-easl2023.0094.
Full textAbstract:
The purpose of this study is to present a generalized class of estimators using the three-stage Optional Randomized Response Technique (ORRT) in the presence of non-response and measurement errors on a sensitive study variable. The proposed estimator makes use of dual auxiliary information. The expression for the bias and mean square error of the proposed estimator are derived using Taylor series expansion. The proposed estimator’s applicability is proven using real data sets. A numerical study is used to compare the efficiency of the proposed estimator with adapted estimators of the finite population mean. The suggested estimator performs better than adapted ordinary, ratio, and exponential ratio-type estimators in the presence of both non-response and measurement errors. The efficiency of the proposed estimator of population mean declines as the inverse sampling rate, non-response rate, and sensitivity level of the survey question increase.