A Study on the Comparison of the Effectiveness of the Jackknife Method in the Biased Estimators

Purpose – Studies in criminal psychology are inevitably undertaken in a context of uncertainty. One class of methods addressing such uncertainties is Monte Carlo (MC) simulation. The purpose of this paper is to provide an introduction to MC simulation for representing uncertainty and focusses on likely uses in studies of criminology and psychology. In addition to describing the method and providing a step-by-step guide to implementing a MC simulation, this paper provides examples using the Fragile Families and Child Wellbeing Survey data. Results show MC simulations can be a useful technique to test biased estimators and to evaluate the effect of bias on power for statistical tests. Design/methodology/approach – After describing MC simulation methods in detail, this paper provides a step-by-step guide to conducting a simulation. Then, a series of examples are provided. First, the authors present a brief example of how to generate data using MC simulation and the implications of alternative probability distribution assumptions. The second example uses actual data to evaluate the impact that omitted variable bias can have on least squares estimators. A third example evaluates the impact this form of heteroskedasticity can have on the power of statistical tests. Findings – This study shows MC simulated variable means are very similar to the actual data, but the standard deviations are considerably less in MC simulation-generated data. Using actual data on criminal convictions and income of fathers, the authors demonstrate the impact of omitted variable bias on the standard errors of the least squares estimator. Lastly, the authors show the p-values are systematically larger and the rejection frequencies correspondingly smaller in heteroskedastic error models compared to a model with homoskedastic errors. Originality/value – The aim of this paper is to provide a better understanding of what MC simulation methods are and what can be achieved with them. A key value of this paper is that the authors focus on understanding the concepts of MC simulation for researchers of statistics and psychology in particular. Furthermore, the authors provide a step-by-step description of the MC simulation approach and provide examples using real survey data on criminal convictions and economic characteristics of fathers in large US cities.

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Prior Variances and Depth Un-Biased Estimators in EEG Focal Source Imaging

EMBEC & NBC 2017 - IFMBE Proceedings ◽

10.1007/978-981-10-5122-7_9 ◽

2017 ◽

pp. 33-36 ◽

Cited By ~ 1

Author(s):

A. Koulouri ◽

V. Rimpiläinen ◽

M. Brookes ◽

J. P. Kaipio

Keyword(s):

Source Imaging ◽

Biased Estimators

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The jackknife method

Universitext - Introduction to Robust and Quasi-Robust Statistical Methods ◽

10.1007/978-3-642-69389-2_4 ◽

1983 ◽

pp. 55-77 ◽

Cited By ~ 1

Author(s):

William J. J. Rey

Keyword(s):

Jackknife Method

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On Jackknifing in Estimating The Finite End–Points of a Distribution*

Calcutta Statistical Association Bulletin ◽

10.1177/0008068319770102 ◽

1977 ◽

Vol 26 (1-4) ◽

pp. 17-24 ◽

Cited By ~ 1

Author(s):

Pranab Kumar Sen

Keyword(s):

Extreme Values ◽

Regular Case ◽

End Points ◽

Biased Estimators

Sample extreme values are biased estimators of the end-points of a distribution, and hence, jackknifing is useful. However, the properties of jackknifing in such a case differ considerably from those in the regular case. These are studied here. Along with a modification of jackknifing, some applications are also considered.

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Catering Information Needs from Global to Local Scales—Potential and Challenges with National Forest Inventories

Forests ◽

10.3390/f10090800 ◽

2019 ◽

Vol 10 (9) ◽

pp. 800 ◽

Cited By ~ 2

Author(s):

Kangas ◽

Räty ◽

Korhonen ◽

Vauhkonen ◽

Packalen

Keyword(s):

Information Needs ◽

National Forest ◽

Model Errors ◽

Sampling Errors ◽

Small Areas ◽

Model Based ◽

Forest Inventories ◽

National Forest Inventories ◽

Biased Estimators ◽

National Information

Forest information is needed at global, national and local scales. This review aimed at providing insights of potential of national forest inventories (NFIs) as well as challenges they have to cater to those needs. Within NFIs, the authors address the methodological challenges introduced by the multitude of scales the forest data are needed, and the challenges in acknowledging the errors due to the measurements and models in addition to sampling errors. Between NFIs, the challenges related to the different harmonization tasks were reviewed. While a design-based approach is often considered more attractive than a model-based approach as it is guaranteed to provide unbiased results, the model-based approach is needed for downscaling the information to smaller scales and acknowledging the measurement and model errors. However, while a model-based inference is possible in small areas, the unknown random effects introduce biased estimators. The NFIs need to cater for the national information requirements and maintain the existing time series, while at the same time providing comparable information across the countries. In upscaling the NFI information to continental and global information needs, representative samples across the area are of utmost importance. Without representative data, the model-based approaches enable provision of forest information with unknown and indeterminable biases. Both design-based and model-based approaches need to be applied to cater to all information needs. This must be accomplished in a comprehensive way In particular, a need to have standardized quality requirements has been identified, acknowledging the possibility for bias and its implications, for all data used in policy making.

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