Evaluation of Simultaneous Equation Techniques in the Presence of Misspecification Error: A Monte Carlo Approach

One  of  the  assumptions  of  Classical  Linear  Regression  Model (CLRMA),  is  that  the  regression  model be  ‘correctly’   specified.  If  the  model  is  not  ‘correctly’  specified,  the  problem  of  model  misspecification  error arises. The objective of the study is to know the performances of the estimator and also the estimator that is greatly affected by misspecification error due to omission of relevant explanatory variable.  Four simultaneous equation techniques (OLS, 2SLS, 3SLS, LIML) were applied to a two-equation model and investigated on their performances when plagued with the problem of misspecification error. A Monte Carlo method simulation method was employed to investigate the effect of these estimators due to misspecification of the model. The findings revealed that the estimates obtained by 2SLS and 3SLS are similar and variances by all the estimates reduced consistently as the sample size increases. The study had revealed that 2 3 SLS performed best using average of parameter criterion while OLS generated the least variances. LIML is mostly affected by misspecification. Keywords: Monte Carlo, Misspecification error, Simultaneous equation

Mitigating Turbulence-Induced Fading in Coherent FSO Links: An Adaptive Space-Time Code Approach

Free space optical communication systems have witnessed a significant rise in attention over the last half a decade owing largely to their enormous bandwidth and relative ease of deployment. Generally, free space optical communication systems differ in their detection mechanism as various detection mechanisms are being reported, including intensity modulation/direct detection FSO, differential FSO and coherent FSO. In this chapter, we explore the prospect of obtaining an optimally performing FSO system by harnessing the cutting-edge features of coherent FSO systems and the coding gain and diversity advantage offered by a four-state space-time trellis code (STTC) in order to combat turbulence-induced fading which has thus far beleaguered the performance of FSO systems. The initial outcomes of this technique are promising as a model for various visible light communication applications

Optimetric analysis of 1x4 array of circular microwave patch antennas for mammographic applications using adaptive gradient descent algorithm

Interest in the use of microwave equipment for breast imagery is on the increase owing to its safety, ease of use and friendlier cost. However, some of the pertinent blights of the design and optimization of microwave antenna include intensive consumption of computing resources, high price of software acquisition and very large optimization time. This paper therefore attempts to address these concerns by devising a rapid means of designing and optimizing the performance of a 1×4 array of circular microwave patch antenna for breast imagery applications by deploying the adaptive gradient descent algorithm (AGDA) for a circumspectly designed artificial neural network. In order to cross validate the findings of this work, the results obtained using the adaptive gradient descent algorithm was compared with those obtained with the deployment of the much reported Levenberg-Marquardt algorithm for the same dataset over same frequency range and training constraints. Analysis of the performance of the AGDA neural network shows that the approach is a viable and accurate technique for rapid design and analysis of arrays of circular microwave patch antenna for breast imaging

Estimation of Garch Models for Nigerian Exchange Rates Under Non-Gaussian Innovations

Financial series often displays evidence of leptokurticity and in that case, the empirical distribution often fails normality.  GARCH models were initially based on normality assumption but estimated model based on this assumption cannot capture all the degree of leptokurticity in the return series. In this paper, we applied variants of GARCH models under non-normal innovations-t-distribution and Generalized Error Distribution (GED) on selected Nigeria exchange rates. The Berndt, Hall, Hall, Hausman (BHHH) numerical derivatives applied in the estimation of models converged faster and the time varied significantly across models. Asymmetric GARCH model with t-distribution (GARCH-t) was selected in most of the cases whereas for Nigeria-US Dollar exchange rate, GARCH-GED was specified. Both distributions showed evidence of leptokurticity in Naira exchange rate return series. The result is of practical importance to practitioners. Key Words: GARCH, Exchange rate, Model specification, Non-Gaussian distribution.

Optimetric analysis of 1x4 array of circular microwave patch antennas for mammographic applications using adaptive gradient descent algorithm

Interest in the use of microwave equipment for breast imagery is on the increase owing to its safety, ease of use and friendlier cost. However, some of the pertinent blights of the design and optimization of microwave antenna include intensive consumption of computing resources, high price of software acquisition and very large optimization time. This paper therefore attempts to address these concerns by devising a rapid means of designing and optimizing the performance of a 1×4 array of circular microwave patch antenna for breast imagery applications by deploying the adaptive gradient descent algorithm (AGDA) for a circumspectly designed artificial neural network. In order to cross validate the findings of this work, the results obtained using the adaptive gradient descent algorithm was compared with those obtained with the deployment of the much reported Levenberg-Marquardt algorithm for the same dataset over same frequency range and training constraints. Analysis of the performance of the AGDA neural network shows that the approach is a viable and accurate technique for rapid design and analysis of arrays of circular microwave patch antenna for breast imaging

DYNAMIC MODELING AND SIMULATION OF SHIRORO HYDROPOWER PLANT IN NIGERIA USING MATLAB/SIMULINK

Hydroelectricity is an important component of world renewable energy supply and hydropower remains a major source of electricity generation due to its environmental friendly nature. This paper aimed at modeling and simulating hydropower plant with a view of increasing the efficiency and stability of the generating station. The hydropower plant model was developed using Matlab/Simulink software. The designed model comprises: Hydraulic turbine (PID governor, servomotor and turbine), Synchronous generator and an excitation system. The dynamic response of the system to the disturbances on the system network was studied. A three phase fault was introduced in the SHPP model at 0.1 sec and cleared at 0.2 sec. The simulated result shows that the generated voltage quickly regained its stability on the removal of the fault, the stator currents went into transient after the fault was cleared and become stable at 0.4 sec. The excitation voltage also regains its stability but it was slower and the speed of the rotor was out of stable after the occurrence of the disturbance on the system. The simulated result shows an improvement in the static and dynamic behavior of SHPP and an increase in the generating performance of the generating station

Neural Network Predictive Controller for Improved Operational Efficiency of Shiroro Hydropower Plant

The development of e fficient models and controllers is central to better understanding and analysis of operational efficiency of modern hydropower plants. In this work, an intelligent Levenberg - Marquardt b ased Neural Network Predictive C ontroller (NNPC) was developed for Shiroro hydroelectric power station us ing actual data obtained from the plant operation . Results obtained after training and simulation of the system show that neural network technique serves as an efficient approach of designing hydroelectric power station models and controllers

An Improved Transmission Equation under Environmental Influences

Radio frequency (RF) communication channel severely suffers from tropospheric scintillation fading caused by the dynamic nature of the atmospheric conditions thereby impairing i ts performance and availability : this induced channel fading effect must be accounted for in the link transmission equation. In this paper, we have proposed an improved transmission link equation by taking into account scintillation fading effect a nd magnitude of the refractive - index structure parameter that play very important role in l inks calculations. This transmission model provides the basis for communication engineers a platform to work with in the link budgetary for planning and design of lo w margin systems of free space communication lin

A Mathematical Modeling Approach for Optimal Trade-offs in a Wireless Sensor Network for a Granary Monitoring System

Wireless sensor networks can be deployed in the monitoring of granary systems and greenhouses. In ensuring the efficiency and reliability of such systems, optimal trade-offs should be guaranteed between the various considered constraints. This work has the important aim of translating the monitoring of the environmental factors that may influence the quality of stored agricultural grains into a mathematical model, in which optimal trade-offs are achieved between coverage efficiency, reduced costs and real-time monitoring. The intention is to mathematically model and optimize a developed distributed wireless sensor network system for quality bulk grains storability. The proposed model shows promise, as it attained optimal levels, with a coverage efficiency of 89% with minimum number of nodes

Bayesian method for solving the problem of multicollinearity in regression

The popular method of estimation in regression, Ordinary Least Squares (OLS) often displays inefficiency especially with large variances and wide confidence intervals thereby making precise estimate difficult when there is strong multicollinearity. Bayesian method of estimation is expected to improve the efficiency of estimated regression model when there is relevant prior information and belief of situation being modelled is available. This study however provided an alternative approach to OLS when there is almost perfect multicollinearity while its performance were compared with the aid of simulation approach to OLS estimator. Results of the simulation study indicate that with respect to Mean Squared Error (MSE) criterion and other criteria, the proposed method perform better than OLS.Keywords: Multicollinearity, Regression, Standard Error, SimulationAMS 2010 Mathematics Subject Classification: 62F15, 62GO5, 62H1