Some Variations on Standard Income Measurement Error Models

2004 ◽  
Author(s):  
Brigham B. Frandsen ◽  
James B. McDonald
Keyword(s):  
Measurement Error ◽  
Gini Coefficient ◽  
Measurement Error Models ◽  
The Gini Coefficient ◽  
Parametric Specification ◽  
Income Measurement ◽  
The Difference ◽  
Multiplicative Measurement Error ◽  
The Relationship ◽  
Measures Of Inequality

Measurement error can have a significant impact on measures of inequality. Using a fairly flexible parametric specification of an independent multiplicative measurement error (IMME) model we explore the relationship between changes in the variance of measurement error, for a given mean of measurement error, on the Gini Coefficient. While the measured Gini is greater than the true Gini, the difference decreases as the variance of measurement error decreases. Copulas are used to relax the assumption of independence of measurement error and true income. In this case the measured Gini can be larger or smaller than the true Gini, depending on the correlation between true income and measurement error. Using the same approach with simulations the effect of a different distribution of measurement error is investigated.

Measurement error-filtered machine learning in digital soil mapping

2021 ◽  
Author(s):  
Stephan van der Westhuizen ◽  
Gerard Heuvelink ◽  
David Hofmeyr
Keyword(s):  
Machine Learning ◽  
Measurement Error ◽  
Soil Property ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Simulation Studies ◽  
True Value ◽  
Linear Relationships ◽  
The Difference ◽  
The Relationship

<p>Digital soil mapping (DSM) may be defined as the use of a statistical model to quantify the relationship between a certain observed soil property at various geographic locations, and a collection of environmental covariates, and then using this relationship to predict the soil property at locations where the property was not measured. It is also important to quantify the uncertainty with regards to prediction of these soil maps. An important source of uncertainty in DSM is measurement error which is considered as the difference between a measured and true value of a soil property.</p><p>The use of machine learning (ML) models such as random forests (RF) has become a popular trend in DSM. This is because ML models tend to be capable of accommodating highly non-linear relationships between the soil property and covariates. However, it is not clear how to incorporate measurement error into ML models. In this presentation we will discuss how to incorporate measurement error into some popular ML models, starting with incorporating weights into the objective function of ML models that implicitly assume a Gaussian error. We will discuss the effect that these modifications have on prediction accuracy, with reference to simulation studies.</p>

Is a Trait Really the Mean of States?

2012 ◽  
Vol 33 (3) ◽  
pp. 131-137 ◽  
Author(s):  
Adam A Augustine ◽  
Randy J. Larsen
Keyword(s):  
Measurement Error ◽  
Personality Trait ◽  
Predictive Utility ◽  
Discrepancy Theory ◽  
The Mean ◽  
The Difference ◽  
Self Discrepancy ◽  
Assessment Strategies ◽  
The Relationship ◽  
Mean State

Although several definitions exist, a personality trait can be defined as the average or expected value of personality-relevant behaviors. However, recent evidence suggests that, while trait questionnaires and aggregated momentary assessments of personality are highly related, they may also differ in meaningful ways. In this study, we examine the relationship between trait and mean state personality. Results indicate that these two assessment strategies, although highly related, do not show convergence (r = .39–.64) levels that would signify an equity of constructs. In line with this, these two assessment strategies show differential predictive utility. Although the pattern of this differential predictive utility suggests that measurement error may account for differences, the difference between trait and mean state personality predicts affect in a manner consistent with self-discrepancy theory. Thus, although these two constructs are highly related, the differences between trait and mean state personality are meaningful.

scholarly journals Commentary: Measuring the shape of degree distributions

2013 ◽  
Vol 1 (2) ◽  
pp. 213-225 ◽  
Author(s):  
JENNIFER M. BADHAM
Keyword(s):  
Gini Coefficient ◽  
Degree Distribution ◽  
Fundamental Property ◽  
Standard Measure ◽  
Gini Coefficients ◽  
The Gini Coefficient ◽  
Different Types ◽  
Network Properties ◽  
The Relationship

AbstractDegree distribution is a fundamental property of networks. While mean degree provides a standard measure of scale, there are several commonly used shape measures. Widespread use of a single shape measure would enable comparisons between networks and facilitate investigations about the relationship between degree distribution properties and other network features. This paper describes five candidate measures of heterogeneity and recommends the Gini coefficient. It has theoretical advantages over many of the previously proposed measures, is meaningful for the broad range of distribution shapes seen in different types of networks, and has several accessible interpretations. While this paper focuses on degree, the distribution of other node-based network properties could also be described with Gini coefficients.

scholarly journals Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Lifeng Wu ◽  
Kai Cai ◽  
Yan Chen
Keyword(s):  
Gini Coefficient ◽  
Northwest China ◽  
Grey Model ◽  
Effective Control ◽  
Grey Prediction ◽  
Gini Coefficients ◽  
The Gini Coefficient ◽  
The Rich ◽  
The Relationship ◽  
Pm2.5 Concentration

To explore the relationship between the PM2.5 concentration and the gap between the rich and the poor, the PM2.5 concentration in 26 provincial regions of China is predicted by using the Gini coefficient as the independent variable. The nonequigap fractional grey prediction model (CFNGM (1, 1)) is used for data fitting and predicting. The validity of the model is verified by comparing with the traditional nonequidistant grey model. The predicting results show that the PM2.5 concentration in many provinces of China presents a roughly downward trend. In the past nine years, the Gini coefficients have declined in more than 70% of the 26 provinces. However, the development of the Gini coefficient in Northwest China fluctuates greatly and even has an upward trend in recent years. According to the predictive results, reasonable suggestions can be put forward for the effective control of PM2.5 emission in China.

Inequalities and political populism: The case of Bulgaria

SEER ◽  
2020 ◽  
Vol 23 (2) ◽  
pp. 233-244
Author(s):  
Lyuboslav Kostov
Keyword(s):  
Eastern Europe ◽  
Gini Coefficient ◽  
Central And Eastern Europe ◽  
Political Rhetoric ◽  
Macroeconomic Shocks ◽  
Theoretical Literature ◽  
The Gini Coefficient ◽  
The Relationship

The article evaluates and analyses the dynamics of inequalities in Bulgaria during 2010-2020 as quantified by a set of particular indicators including the Gini coefficient, the S80/S20 indicator and the share of income held by the richest five per cent. The article examines the relationship between these inequalities and the growth of a certain type of political rhetoric which the literature clearly categorises as populism and which has been rising in central and eastern Europe as in other places elsewhere. In addition, the most up-to-date theoretical literature on these issues is studied and summarised. Social and macroeconomic shocks evidently affect the development of inequalities and, with the global Covid-19 pandemic, we are in the middle of one such set of shocks. The article concludes that a broad public and expert debate is overdue on the problems of inequalities and the consequences of their growth - namely: the development of populist rhetoric - and that reforms are required to reduce inequalities to within parameters that are more socially acceptable as a means of reducing the incidence of populism.

scholarly journals Effects of plot size, stand density, and scan density on the relationship between airborne laser scanning metrics and the Gini coefficient of tree size inequality

2017 ◽  
Vol 47 (12) ◽  
pp. 1590-1602 ◽  
Author(s):  
Syed Adnan ◽  
Matti Maltamo ◽  
David A. Coomes ◽  
Rubén Valbuena
Keyword(s):  
Spatial Resolution ◽  
Gini Coefficient ◽  
Laser Scanning ◽  
Stand Density ◽  
Airborne Laser Scanning ◽  
Large Field ◽  
Plot Size ◽  
Airborne Laser ◽  
The Gini Coefficient ◽  
The Relationship

Estimation of the Gini coefficient (GC) of tree sizes using airborne laser scanning (ALS) can provide maps of forest structure across the landscape, which can support sustainable forest management. A challenge arises in determining the optimal spatial resolution that maximizes the stability and precision of GC estimates, which in turn depends on stand density or ALS scan density. By subsampling different plot sizes within large field plots, we evaluated the optimal spatial resolution by observing changes in GC estimation and in its correlation with ALS metrics. We found that plot size had greater effects than either stand density or ALS scan density on the relationship between GC and ALS metrics. Uncertainty in GC estimates fell as plot size increased. Correlation with ALS metrics showed convex curves with maxima at 250–450 m2, which thus was considered the optimal plot size and, consequently, the optimal spatial resolution. By thinning the density of the ALS point cloud, we deduced that at least 3 points·m−2were needed for reliable GC estimates. Many nationwide ALS scan densities are sparser than this, so may be unreliable for GC estimation. Ours is a simple approach for evaluating the optimal spatial resolution in remote sensing estimation of any forest attribute.

scholarly journals Group averaging and the Gini deviation

2021 ◽  
Vol 29 (3) ◽  
pp. 595-605
Author(s):  
Oleg I. Pavlov ◽  
Olga Yu. Pavlova
Keyword(s):  
Gini Coefficient ◽  
Upper Bound ◽  
Lorenz Curve ◽  
Piecewise Linear ◽  
Geometric Parameters ◽  
Natural Question ◽  
Lorenz Curves ◽  
The Gini Coefficient ◽  
The Difference ◽  
Group Averaging

It is known that partitioning a society into groups with subsequent averaging in each group decreases the Gini coefficient. The resulting Lorenz function is piecewise linear. This study deals with a natural question: by how much the Gini coefficient could decrease when passing to a piecewise linear Lorenz function? Obtained results are quite illustrative (since they are expressed in terms of the geometric parameters of the polygon Lorenz curve, such as the lengths of its segments and the angles between successive segments) upper bound estimates for the maximum possible change in the Gini coefficient with a restriction on the group shares, or on the difference between the averaged values of the attribute for consecutive groups. It is shown that there exist Lorenz curves with the Gini coefficient arbitrarily close to one, and at the same time with the Gini coefficient of the averaged society arbitrarily close to zero.

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