scholarly journals D-optimal Design in Linear Model With Different Heteroscedasticity Structures

2020 ◽  
Vol 9 (2) ◽  
pp. 7 ◽  
Author(s):  
BODUNWA, O. K. ◽  
FASORANBAKU, O. A.
Keyword(s):  
Optimal Design ◽  
Linear Model ◽  
Extreme Values ◽  
Real Life ◽  
Simulated Data ◽  
Least Square Method ◽  
Correction Method ◽  
Least Square ◽  
Sequential Method ◽  
Explanatory Variables

In this paper, we developed D-optimal design in linear model with two explanatory variables in the presence of heteroscedasticity. A sequential method of getting D-optimal design was adopted. Two different structures were used based on the literatures; it was found that the optimal design takes the extreme values of the design region. The results of simulated data was justified with real life data from the kinematic viscosity of a lubricant, in stokes, as a function of temperature and pressure which was used as discussed in Linssen (1975). The relative efficiency of other designs with respect to D-optimal designs was determined. Three correction methods was adopted from weighted least square method for heteroscedasticity problem, it was found that the correction method tagged HCW1 performed better.

scholarly journals Impulse of Dividend Payment Decision: Evidence from Pharmaceutical Industry in Bangladesh

2017 ◽  
Vol 9 (1) ◽  
pp. 219
Author(s):  
Ariful Hoque
Keyword(s):  
Stock Exchange ◽  
Secondary Data ◽  
Least Square Method ◽  
Agency Cost ◽  
Least Square ◽  
Pharmaceutical Companies ◽  
Financial Leverage ◽  
Dividend Payment ◽  
Explanatory Variables ◽  
Prime Objective

The dividend is the reward of shareholders of an organization in exchange for time and risk. For maximizing shareholder’s wealth, optimum dividend payout ratio is essential. The prime objective of this paper is to identify impulse of dividend payment decision of listed pharmaceutical companies in Dhaka Stock Exchange of Bangladesh. Dividend payment decision is the dependent variable and profitability, firm’s size, financial leverage, growth, and agency costs are taken as explanatory variables in this study. Collected secondary data are analyzed by econometrics software Eviews 8 through least square method. Formulated multiple regression models show value of R-square (R2) is 0.604817. R-square (R2) value indicates explanatory variables explain 60.48% variation of the dependent variable. The study also reveals that profitability and agency cost positively influence the dividend payment decision and firm’s size, financial leverage, growth negatively impact on the dividend payment decision of selected pharmaceutical companies. Among explanatory variables, profitability is not statistically significant at 5% significant level whereas firm’s size, financial leverage, growth and agency cost are found statistically significant at 5% significant level. So this paper finds that listed pharmaceutical companies in Dhaka Stock Exchange must consider firm’s size, financial leverage, growth and agency cost in their dividend payment decision.

scholarly journals An Improved Sequential Initiation Method for Multitarget Track in Clutter with Large Noise Measurement

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Daxiong Ji ◽  
Dongdong Li
Keyword(s):  
Least Square Method ◽  
Initial Position ◽  
Measured Data ◽  
Least Square ◽  
Multiple Objects ◽  
Detection Distance ◽  
Sequential Method ◽  
Sonar Sensors ◽  
Underwater Environment ◽  
Linear Least Square

This paper proposes an improved sequential method for underwater multiple objects tracks initiation in clutter, estimating the initial position for the trajectory. The underwater environment is complex and changeable, and the sonar data are not very ideal. When the detection distance is far, the error of measured data is also great. Besides that, the clutter has a grave effect on the tracks initiation. So it is hard to initialize a track and estimate the initial position. The new tracks initiation is that when at least six of ten points meet the requirements, then we determine that there is a new track and the initial states of the parameters are estimated by the linear least square method. Compared to the conventional tracks initiation methods, our method not only considers the kinematics information of targets, but also regards the error of the sonar sensors as an important element. Computer simulations confirm that the performance of our method is very nice.

scholarly journals Women labor participation in rice production in some selected areas of Thakurgaon district

2015 ◽  
Vol 2 (2) ◽  
pp. 239-246
Author(s):  
Asha Roy ◽  
Dilshad Zahan Ethen ◽  
Riffat Ara Zannat Tama ◽  
Ismat Ara Begum
Keyword(s):  
Skill Acquisition ◽  
Least Square Method ◽  
Acquisition Training ◽  
Rice Production ◽  
Least Square ◽  
Farm Size ◽  
Labor Participation ◽  
Structured Interviews ◽  
Explanatory Variables ◽  
Participation Of Women

The present study was undertaken to analyze the participation of women labor in rice production activities covering randomly selected 50 rural households of two villages under Ranisonkail Upazila of Thakurgaon district. Data were collected from the selected households using face to face semi-structured interviews during December 2013 to February 2014. The study revealed that in rice production activities the percentage of hiring out days for women were 94.18 and 92.90 in aman and boro seasons, respectively. To determine the effects of the explanatory variables on women labor participation in rice production, Ordinary Least Square method was used. The analysis showed that age of the respondents and total household expenditure were positively related with women participation but negatively related with education, number of male earning members and farm size of the households. The study concludes that education, credit facilities, extension and motivation, need-based training should have the potential to increase women’s participation in farm activities reasonably contributed to household income. Therefore, effective initiatives undertaken by the concerned agencies in improving women’s education, skill acquisition training and access to information could enhance women’s empowerment in order to achieve gender equality and development at all levels in the rural society of Bangladesh.Res. Agric., Livest. Fish.2(2): 239-246, August 2015

scholarly journals A Procedure to Fit an Interpolating Curve to a Set of Logistic Data

2017 ◽  
Vol 65 (2) ◽  
pp. 103-105
Author(s):  
Md Shariful Islam ◽  
Mir Shariful Islam ◽  
AFM Khodadad Khan ◽  
Md Zavid Iqbal Bangalee
Keyword(s):  
Population Dynamics ◽  
Numerical Analysis ◽  
Logistic Model ◽  
Real Life ◽  
Least Square Method ◽  
Least Square ◽  
Life Problems

Logistic dynamics are frequently encountered in real life problems, especially in population dynamics. Data showing an appearance to follow logistic model may be interpolated by standards methods in numerical analysis. In this paper we discuss a method to fit a curve to such data using the intrinsic analytic properties of the data in terms of least square method and graphic tools in the environment of Mathematica. Dhaka Univ. J. Sci. 65(2): 103-105, 2017 (July)

scholarly journals COMPARISON OF THE LINEAR AND THE NON-LINEAR BLOCK ADJUSTMENT ON SATELLITE IMAGES RESEARCH

2018 ◽  
Vol XLII-3 ◽  
pp. 775-780
Author(s):  
C. Li ◽  
C. Chen ◽  
Z. Guo ◽  
Q. Liu
Keyword(s):  
Linear Model ◽  
Satellite Images ◽  
Satellite Image ◽  
Least Square Method ◽  
Least Square ◽  
Image Block ◽  
Function Model ◽  
Check Points ◽  
Non Linear ◽  
Linear Least Square

The Rational Function Model （RFM） is a non-linear model. Usually, the RFM-based satellite image block adjustment uses the Taylor series to expand error equations, and then solves the linear model. Theoretically, linearization of a non-linear model affects the accuracy and reliability of the adjustment result. This paper presents linear and non-linear methods for solving the RFM-based block adjustment,and takes ZiYuan3 (ZY-3) satellite imagery block adjustment as an example, using same check points to assess the accuracy of the two methods. In this paper, a non-linear least square method is used for solving the RFM-based block adjustment, which expands a solution to the block adjustment.

scholarly journals Modeling and parameters estimation of a Spatial Predator-Prey distribution

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Denis Ndanguza ◽  
Jean Pierre Muhirwa ◽  
Anatholie Uwimana
Keyword(s):  
Stability Analysis ◽  
Synthetic Data ◽  
Simulated Data ◽  
Least Square Method ◽  
Parameters Estimation ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Predator Prey ◽  
Monte Carlo Algorithms

Predator prey interactions are important in ecology and most of time in the analysis, the two antagonists are assumed to be in a closed system. The aim of this study is to model the unclosed predator-prey system. The model is built and simulated data are computed by adding noise on deterministic solution. Therefore, model parameters are estimated using least square method. We compute the two critical points and the stability analysis is carried out and results show that the population is stable at one critical point and unstable at (0,0). The model fits the synthetic data with coefficient of determination R2 = 0.9693 equivalent to 96.93%. Using the residual analysis to test the validity of the model, it is shown that there is no pattern between residuals. To strengthen the validity of the model, the Markov Chain Monte Carlo algorithms are used as an alternative method in parameters estimation. Diagnostics prove the chains’ convergence which is the sign of an accurate model. As conclusion, the model is accurate and it can be applied to real data.Keywords: predator-prey, spatial distribution, parameters, Metropolis-Hastings algorithm, model diagnostic, stability analysis

scholarly journals Parametric Design Optimization of Uncertain Ordinary Differential Equation Systems

2011 ◽  
Author(s):  
Joe Hays ◽  
Adrian Sandu ◽  
Corina Sandu ◽  
Dennis Hong
Keyword(s):  
Dynamical Systems ◽  
Optimal Design ◽  
Initial Conditions ◽  
Practical Importance ◽  
Real Life ◽  
Parametric Uncertainty ◽  
Least Square ◽  
Optimization Process ◽  
Entire Family ◽  
New Framework

This work presents a novel optimal design framework that treats uncertain dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. The inclusion of uncertainty in design is of paramount practical importance because all real-life systems are affected by it. Designs that ignore uncertainty often lead to poor robustness and suboptimal performance. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The uncertainty statistics are explicitly included in the optimization process. Systems that are nonlinear, have active constraints, or opposing design objectives are shown to benefit from the new framework. Specifically, using a constraint-based multi-objective formulation, the direct treatment of uncertainties during the optimization process is shown to shift, or off-set, the resulting Pareto optimal trade-off curve. A nonlinear vehicle suspension design problem, subject to parametric uncertainty, illustrates the capability of the new framework to produce an optimal design that accounts for the entire family of systems within the associated probability space.

scholarly journals An optical model for deriving the spectral particulate backscattering coefficients in oceanic waters

Ocean Science ◽  
2013 ◽  
Vol 9 (6) ◽  
pp. 987-1001 ◽  
Author(s):  
S. P. Tiwari ◽  
P. Shanmugam
Keyword(s):  
Optical Model ◽  
Trust Region ◽  
Simulated Data ◽  
Correlation Coefficients ◽  
Least Square Method ◽  
Least Square ◽  
Data Set ◽  
New Model ◽  
Trust Region Algorithm ◽  
In Situ Data

Abstract. An optical model is developed based on the diffuse attenuation coefficient (Kd) to estimate particulate backscattering coefficients bbp(λ) in oceanic waters. A large in situ data set is used to establish robust relationships between bbp(530) and bbp(555) and Kd(490) using an efficient nonlinear least-square method which uses the trust region algorithm with Bisquare weights scheme to adjust the coefficients. These relationships are obtained with good correlation coefficients (R2 = 0.786 and 0.790), low root mean square error (RMSE = 0.00076 and 0.00072) and 95% confidence bounds. The new model is tested with three independent data sets: the NOMAD SeaWiFS Match ups, OOXIX IOP algorithm workshop evaluation data set (Version 2.0w APLHA), and IOCCG simulated data set. Results show that the new model makes good retrievals of bbp at all key wavelengths (from 412–683 nm), with statistically significant improvements over other inversion models. Thus, the new model has the potential to improve our present knowledge of particulate matter and their optical variability in oceanic waters.

scholarly journals Statistical Analysis of Nigeria’s Price Sector: An Econometric Approach

2021 ◽  
pp. 29-40
Author(s):  
D. M. O. Omebo ◽  
T. D. Ailobhio ◽  
G. I. Fanen
Keyword(s):  
Predictive Performance ◽  
Least Square Method ◽  
Least Square ◽  
Explanatory Variables ◽  
Ordinary Least Square ◽  
First Order ◽  
Overall Performance ◽  
Econometric Approach ◽  
Good Predictive Performance ◽  
High Measure

This study analyzed Nigeria’s price sector using a formulated model for the price sector of the Nigeria economy. A set of simultaneous equations were used to reflect the implicit gross domestic product deflators for each of the sectors of the Nigeria economy and was found to be over identified under the order condition for identification. The model was estimated by ordinary least square method and two stage least square methods. All the variables have expected signs and as indicated by the F –statistic, the overall performance of the entire regression is significant.  The high measure of R2 and Ṝ2, in each case indicates that the explanatory variables included in the equation jointly account for the entire variation. The small RMSE also indicates that the equations have good fit. Durbin –Watson statistics shows that there is no positive first order autocorrelation. The small value of the Theil’s inequality indicates that the equation has good predictive performance. The researcher therefore recommends that government should employ the model so as to be able to monitor price of each of the sectors of the economy and put proper mechanism in place to control those sectors that affect the overall price sector of the economy.

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