scholarly journals USA Income Distribution Counter-Business-Cyclical Trend

2019 ◽  
Vol 4 (2) ◽  
pp. 11-26
Author(s):  
Bijan Bidabad
Keyword(s):  
Income Distribution ◽  
Business Cycles ◽  
Continuous Functions ◽  
L1 Norm ◽  
Statistical Population ◽  
Regression Parameters ◽  
Estimation Problems ◽  
Two Parameters ◽  
Cyclical Trend ◽  
Continuous Estimation

In this paper, the L1 norm of continuous functions and corresponding continuous estimation of regression parameters are defined. The continuous L1 norm estimation problems of linear one and two parameters models are solved. We proceed to use the functional form and parameters of the probability distribution function of income to exactly determine the L1 norm approximation of the corresponding Lorenz curve of the statistical population under consideration. U.S. economic data used to estimate income distribution. An interesting finding of these calculations is that the distribution of income obeys counter-wise business cycles fluctuations. This finding is a new area for research in the realm of the theory and application of income distribution and business cycles interrelationship.

scholarly journals Estimating Lorenz Curve for Iran by Using Continuous L1 Norm Estimation

2019 ◽  
Vol 3 (1) ◽  
pp. 11-21
Author(s):  
Bijan Bidabad
Keyword(s):  
Lorenz Curve ◽  
Functional Form ◽  
Linear Models ◽  
Continuous Functions ◽  
L1 Norm ◽  
Statistical Population ◽  
Regression Parameters ◽  
Two Parameters ◽  
Continuous Estimation ◽  
Norm Approximation

In this paper, the L1 norm of continuous functions and corresponding continuous estimation of regression parameters are defined. The continuous L1 norm estimation problem of one and two parameters linear models in the continuous case are solved. We proceed to use the functional form and parameters of the probability distribution function of income to exactly determine the L1 norm approximation of the corresponding Lorenz curve of the statistical population under consideration. Iran family budget data were used to estimate income distribution for the period of 1362-1370.  

scholarly journals Continuous L1 Norm Estimation of Lorenz Curve

2019 ◽  
Vol 1 (1) ◽  
pp. 41-49
Author(s):  
Bijan Bidabad
Keyword(s):  
Lorenz Curve ◽  
Functional Form ◽  
Linear Models ◽  
Continuous Functions ◽  
L1 Norm ◽  
Statistical Population ◽  
Regression Parameters ◽  
Two Parameters ◽  
Continuous Estimation ◽  
Norm Approximation

In this paper, the L1 norm of continuous functions and corresponding continuous estimation of regression parameters are defined. The continuous L1 norm estimation problem of one and two parameters linear models in the continuous case is solved. We proceed to use the functional form and parameters of the probability distribution function of income to exactly determine the L1 norm approximation of the corresponding Lorenz curve of the statistical population under consideration.    

scholarly journals New Algorithms for L1 Norm Regression

2019 ◽  
Vol 1 (1) ◽  
pp. 1-18
Author(s):  
Bijan Bidabad
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Simple Linear Regression ◽  
L1 Norm ◽  
Median Problem ◽  
Regression Parameters ◽  
Multiple Regression Models ◽  
Weighted Median ◽  
Weighted Median Problem ◽  
New Algorithms

In this paper, we propose four algorithms for L1 norm computation of regression parameters, where two of them are more efficient for simple and multiple regression models.  However, we start with restricted simple linear regression and corresponding derivation and computation of the weighted median problem. In this respect, a computing function is coded.  With discussion on the m parameters model, we continue to expand the algorithm to include unrestricted simple linear regression, and two crude and efficient algorithms are proposed. The procedures are then generalized to the m parameters model by presenting two new algorithms, where the algorithm 4 is selected as more efficient. Various properties of these algorithms are discussed.

scholarly journals Unbiased Phenotype Detection Using Negative Controls

2019 ◽  
Vol 24 (3) ◽  
pp. 234-241 ◽  
Author(s):  
Antje Janosch ◽  
Carolin Kaffka ◽  
Marc Bickle
Keyword(s):  
Sparse Matrix ◽  
Complex Data ◽  
L1 Norm ◽  
Hide Information ◽  
Novel Method ◽  
Two Parameters ◽  
Advanced Computing ◽  
Negative Controls ◽  
Comprehensive Measurement ◽  
Sophisticated Analysis

Phenotypic screens using automated microscopy allow comprehensive measurement of the effects of compounds on cells due to the number of markers that can be scored and the richness of the parameters that can be extracted. The high dimensionality of the data is both a rich source of information and a source of noise that might hide information. Many methods have been proposed to deal with this complex data in order to reduce the complexity and identify interesting phenotypes. Nevertheless, the majority of laboratories still only use one or two parameters in their analysis, likely due to the computational challenges of carrying out a more sophisticated analysis. Here, we present a novel method that allows discovering new, previously unknown phenotypes based on negative controls only. The method is compared with L1-norm regularization, a standard method to obtain a sparse matrix. The analytical pipeline is implemented in the open-source software KNIME, allowing the implementation of the method in many laboratories, even ones without advanced computing knowledge.

BETWEEN SCYLLA AND CHARYBDIS: INCOME DISTRIBUTION, CONSUMER CREDIT, AND BUSINESS CYCLES

2018 ◽  
Vol 57 (2) ◽  
pp. 953-971 ◽  
Author(s):  
Alberto Cardaci ◽  
Francesco Saraceno
Keyword(s):  
Income Distribution ◽  
Business Cycles ◽  
Consumer Credit

A Note on Relative Prices and Income Distribution over the US Business Cycle: 1857–2009

2020 ◽  
pp. 048661342096286
Author(s):  
Claudio Alberto Castelo Branco Puty
Keyword(s):  
Income Distribution ◽  
Business Cycles ◽  
Labor Costs ◽  
Market Prices ◽  
Relative Prices ◽  
Industrial Economy ◽  
Us Economy ◽  
The Us ◽  
Unit Labor Costs ◽  
Jel Classifications

This paper investigates the relation between relative prices and the income distribution by examining variations in output and prices occurred over thirty-three US business cycles from 1857 to 2009. Using a broad database, the author shows that average relative prices in twenty-seven industries of the US economy presented a remarkably smaller variation than the corresponding variation in output levels, profits and wages. These time-series results, although not conclusive, may provide additional empirical evidence of the Ricardian claim that even relative market prices in an industrial economy are strongly dominated by the correspondent integrated unit labor costs and that changes along a wage-profit schedule will play only a secondary role in their determination. JEL classifications: E11, E32

Income Distribution and Business Cycles

1992 ◽  
Vol 50 (3) ◽  
pp. 316-332
Author(s):  
William Van Lear
Keyword(s):  
Income Distribution ◽  
Business Cycles

On a Perturbation in a Two-Parameter Ordinary Differential Equation of the Second Order

1971 ◽  
Vol 14 (1) ◽  
pp. 25-33 ◽  
Author(s):  
M. Faierman
Keyword(s):  
Differential Equation ◽  
Ordinary Differential Equation ◽  
Linear System ◽  
Continuous Functions ◽  
Second Order ◽  
The Moment ◽  
Image Position ◽  
Two Parameters ◽  
Two Parameter

Let us consider the linear system in the two parameters λ and μ; i. e.,1.11.2and where for the moment we shall assume both b(x) and q(x) are real-valued, continuous functions in [0, 1].

scholarly journals Optimization methods for l 1 -energy minimization in the estimation of optical flow

2013 ◽  
Vol 29 (1) ◽  
pp. 109-117
Author(s):  
MIRCEA DAN RUS ◽  
Keyword(s):  
Optical Flow ◽  
Energy Minimization ◽  
Optimization Methods ◽  
L1 Norm ◽  
Differential Model ◽  
Flow Models ◽  
Energy Functionals ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Estimation Problems

The aim of this paper is to present a new approach for solving the minimization problem for a large class of energy functionals that appear in the differential models of optical flow estimation problems, and which are expressed using the discrete l1-norm. The choice of l1-energy minimization is motivated by the fact that quadratic l2 optimization is not robust to outliers and that l1-norm is a better choice for modeling real problems involving discrete signals. The method described in this paper is very general, thus the advantage of being applicable to almost every differential model that has been proposed so far for the optical flow estimation problem. In order to test and validate our method, a MATLAB implementation on several optical flow models is currently under development. Also, a multi-core implementation on GP-GPU is to be considered in the near future.

Sign in / Sign up

Export Citation Format

Share Document