scholarly journals On the Performance of Some Biased Estimators in a Misspecified Model with Correlated Regressors

2017 ◽  
Vol 18 (1) ◽  
pp. 27-52 ◽  
Author(s):  
Shalini Chandra ◽  
Gargi Tyagi
Keyword(s):  
Misspecified Model ◽  
Biased Estimators

An introduction to Monte Carlo simulations in criminal psychology: applications in evaluating biased estimators for recidivism

2015 ◽  
Vol 5 (2) ◽  
pp. 149-156 ◽  
Author(s):  
Priscillia Hunt ◽  
Jeremy N.V Miles
Keyword(s):  
Criminal Psychology ◽  
Statistical Tests ◽  
Actual Data ◽  
Content Type ◽  
Criminal Convictions ◽  
Omitted Variable Bias ◽  
Mc Simulation ◽  
Biased Estimators ◽  
Variable Bias ◽  
The Impact

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.

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

2017 ◽  
pp. 33-36 ◽  
Author(s):  
A. Koulouri ◽  
V. Rimpiläinen ◽  
M. Brookes ◽  
J. P. Kaipio
Keyword(s):  
Source Imaging ◽  
Biased Estimators

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

1977 ◽  
Vol 26 (1-4) ◽  
pp. 17-24 ◽  
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.

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

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 800 ◽  
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.

Alternative Bias Approximations in Regressions with a Lagged-Dependent Variable

1993 ◽  
Vol 9 (1) ◽  
pp. 62-80 ◽  
Author(s):  
Jan F. Kiviet ◽  
Garry D.A. Phillips
Keyword(s):  
Least Squares ◽  
Mean Squared Error ◽  
Multiple Linear Regression Model ◽  
Small Sample ◽  
Large Sample ◽  
Squared Error ◽  
Coefficient Vector ◽  
Lagged Dependent Variable ◽  
Biased Estimators ◽  
The One

The small sample bias of the least-squares coefficient estimator is examined in the dynamic multiple linear regression model with normally distributed whitenoise disturbances and an arbitrary number of regressors which are all exogenous except for the one-period lagged-dependent variable. We employ large sample (T → ∞) and small disturbance (σ → 0) asymptotic theory and derive and compare expressions to O(T−1) and to O(σ2), respectively, for the bias in the least-squares coefficient vector. In some simulations and for an empirical example, we examine the mean (squared) error of these expressions and of corrected estimation procedures that yield estimates that are unbiased to O(T−l) and to O(σ2), respectively. The large sample approach proves to be superior, easily applicable, and capable of generating more efficient and less biased estimators.

On biased estimators and the unbiased Cramér-Rao lower bound

1990 ◽  
Vol 21 (4) ◽  
pp. 349-350 ◽  
Author(s):  
Petre Stoica ◽  
Randolph L Moses
Keyword(s):  
Lower Bound ◽  
Cramer Rao Lower Bound ◽  
Biased Estimators

Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments

2015 ◽  
Vol 6 (1-2) ◽  
Author(s):  
Joel A. Middleton ◽  
Peter M. Aronow
Keyword(s):  
Treatment Effect ◽  
Average Treatment Effect ◽  
Unbiased Estimation ◽  
Random Allocation ◽  
Randomized Experiments ◽  
Average Treatment ◽  
Randomized Field Experiment ◽  
The Difference ◽  
Biased Estimators ◽  
Cluster Randomized

AbstractMany estimators of the average treatment effect, including the difference-in-means, may be biased when clusters of units are allocated to treatment. This bias remains even when the number of units within each cluster grows asymptotically large. In this paper, we propose simple, unbiased, location-invariant, and covariate-adjusted estimators of the average treatment effect in experiments with random allocation of clusters, along with associated variance estimators. We then analyze a cluster-randomized field experiment on voter mobilization in the US, demonstrating that the proposed estimators have precision that is comparable, if not superior, to that of existing, biased estimators of the average treatment effect.

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