Simulation Studies of Binomial Sampling: A New Variance Estimator and Density Predictor, with Special Reference to the Russian Wheat Aphid (Homoptera: Aphididae)

1991 ◽  
Vol 84 (1) ◽  
pp. 140-147 ◽  
Author(s):  
G. B. Schaalje ◽  
R. A. Butts ◽  
T. J. Lysyk
Keyword(s):  
Special Reference ◽  
Russian Wheat Aphid ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Binomial Sampling

scholarly journals Parametric bootstrap estimators for hybrid inference in forest inventories

2017 ◽  
Vol 91 (3) ◽  
pp. 354-365 ◽  
Author(s):  
Mathieu Fortin ◽  
Rubén Manso ◽  
Robert Schneider
Keyword(s):  
Multiple Imputation ◽  
Nonlinear Models ◽  
Parametric Bootstrap ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Resampling Methods ◽  
Forest Inventories ◽  
Point Estimator ◽  
Complex Models ◽  
High Level

Abstract In forestry, the variable of interest is not always directly available from forest inventories. Consequently, practitioners have to rely on models to obtain predictions of this variable of interest. This context leads to hybrid inference, which is based on both the probability design and the model. Unfortunately, the current analytical hybrid estimators for the variance of the point estimator are mainly based on linear or nonlinear models and their use is limited when the model reaches a high level of complexity. An alternative consists of using a variance estimator based on resampling methods (Rubin, D. B. (1987). Multiple imputation for nonresponse surveys. John Wiley & Sons, Hoboken, New Jersey, USA). However, it turns out that a parametric bootstrap (BS) estimator of the variance can be biased in contexts of hybrid inference. In this study, we designed and tested a corrected BS estimator for the variance of the point estimator, which can easily be implemented as long as all of the stochastic components of the model can be properly simulated. Like previous estimators, this corrected variance estimator also makes it possible to distinguish the contribution of the sampling and the model to the variance of the point estimator. The results of three simulation studies of increasing complexity showed no evidence of bias for this corrected variance estimator, which clearly outperformed the BS variance estimator used in previous studies. Since the implementation of this corrected variance estimator is not much more complicated, we recommend its use in contexts of hybrid inference based on complex models.

Prediction of Finite Population Proportion When Responses Are Misclassified

2020 ◽  
Author(s):  
Sumanta Adhya ◽  
Surupa Roy ◽  
Tathagata Banerjee
Keyword(s):  
Empirical Study ◽  
Finite Population ◽  
Asymptotic Properties ◽  
Auxiliary Variable ◽  
Binary Response ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Computationally Efficient ◽  
Extensive Simulation ◽  
Population Proportion

Abstract We propose a model-based predictive estimator of the finite population proportion of a misclassified binary response, when information on the auxiliary variable(s) is available for all units in the population. Asymptotic properties of the misclassification-adjusted predictive estimator are also explored. We propose a computationally efficient bootstrap variance estimator that exhibits better performance compared to usual analytical variance estimator. The performance of the proposed estimator is compared with other commonly used design-based estimators through extensive simulation studies. The results are supplemented by an empirical study based on literacy data.

scholarly journals Information criteria for nonlinear time series models

2016 ◽  
Vol 20 (3) ◽  
Author(s):  
Saskia Rinke ◽  
Philipp Sibbertsen
Keyword(s):  
Error Term ◽  
Linear Models ◽  
Nonlinear Models ◽  
General Information ◽  
Information Criteria ◽  
Order Selection ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Linear And Nonlinear ◽  
The Impact

AbstractIn this paper the performance of different information criteria for simultaneous model class and lag order selection is evaluated using simulation studies. We focus on the ability of the criteria to distinguish linear and nonlinear models. In the simulation studies, we consider three different versions of the commonly known criteria AIC, SIC and AICc. In addition, we also assess the performance of WIC and evaluate the impact of the error term variance estimator. Our results confirm the findings of different authors that AIC and AICc favor nonlinear over linear models, whereas weighted versions of WIC and all versions of SIC are able to successfully distinguish linear and nonlinear models. However, the discrimination between different nonlinear model classes is more difficult. Nevertheless, the lag order selection is reliable. In general, information criteria involving the unbiased error term variance estimator overfit less and should be preferred to using the usual ML estimator of the error term variance.

scholarly journals An Approximate Point-based Alternative for the Estimation of Variance under Big BAF Sampling

2020 ◽  
Author(s):  
Thomas B. Lynch ◽  
Jeffrey H Gove ◽  
Timothy G Gregoire ◽  
Mark J Ducey
Keyword(s):  
Forest Inventory ◽  
Variance Estimation ◽  
Basal Area ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Forest Types ◽  
Variance Estimators ◽  
Computer Simulation Studies ◽  
New Formula ◽  
Estimation Of Variance

Abstract BackgroundA new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including \dbh s and heights needed for volume estimation.MethodsThe new estimator is derived using the \Dm\ from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce's formula.ResultsSeveral computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas.ConclusionsA possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the \dbh-height relationship can be affected substantially by density perhaps through competition. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of 1/n where n is the number of sample points.

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