A robust MM estimates for the linear model is revisited. This estimates are defined by a three-stage procedures and posses the following properties: (i) they are highly efficient when the errors have a normal distribution and (ii) their breakdown-point is 0.5. A numerical examples are used to show that the MM estimates has a higher breakdown point and is more efficient than The RLS (Reweighted Least Squares Regression based on The Least Median Squares) estimates.
Suatu penganggar teguh dalam model linear dinamakan Penganggar MM diperkenalkan kembali. Penganggar ini ditakrifkan menerusi pendekatan 3 peringkat dan mempunyai sifat sifat seperti berikut: i) kecekpan yang tinggi sekiranya ralat tertabur secara normal dan ii) titik musnah bersamaan 0.5. Contoh berangka telah digunakan ulituk menunjukkan bahawa penganggar ini mempunyai titik musnah yang tinggi dan lebih cekap daripada penganggar KDTB (Penganggar Kuasadua Terkecil Berpemberat berdasarkan Kaedah Kuasadua Terkecil).
This paper proposes the use of bootstrap, robust and fuzzy multiple linear regressions method in
handling general insurance in order to get improved results. The main objective of bootstrapping is to
estimate the distribution of an estimator or test statistic by resampling one's data or a model estimated
from the data under conditions that hold in a wide variety of econometric applications. In addition,
bootstrap also provides approximations to distributions of statistics, coverage probabilities of confidence
intervals, and rejection probabilities of hypothesis tests that produce accurate results. In this paper, we
emphasize the combining and modelling using bootstrapping, robust and fuzzy regression methodology.
The results show that alternative methods produce better results than multiple linear regressions (MLR)
model.
Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization are of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R 2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR, and RSM models, respectively.
Power system oscillation is a serious threat to the stability of multimachine power systems. The coordinated control of power system stabilizers (PSS) and thyristor-controlled series compensation (TCSC) damping controllers is a commonly used technique to provide the required damping over different modes of growing oscillations. However, their coordinated design is a complex multimodal optimization problem that is very hard to solve using traditional tuning techniques. In addition, several limitations of traditionally used techniques prevent the optimum design of coordinated controllers. In this paper, an alternate technique for robust damping over oscillation is presented using backtracking search algorithm (BSA). A 5-area 16-machine benchmark power system is considered to evaluate the design efficiency. The complete design process is conducted in a linear time-invariant (LTI) model of a power system. It includes the design formulation into a multi-objective function from the system eigenvalues. Later on, nonlinear time-domain simulations are used to compare the damping performances for different local and inter-area modes of power system oscillations. The performance of the BSA technique is compared against that of the popular particle swarm optimization (PSO) for coordinated design efficiency. Damping performances using different design techniques are compared in term of settling time and overshoot of oscillations. The results obtained verify that the BSA-based design improves the system stability significantly. The stability of the multimachine power system is improved by up to 74.47% and 79.93% for an inter-area mode and a local mode of oscillation, respectively. Thus, the proposed technique for coordinated design has great potential to improve power system stability and to maintain its secure operation.
Replicated linear functional relationship model is often used to describe
relationships between two circular variables where both variables have error terms and
replicate observations are available. We derive the estimate of the rotation parameter
of the model using the maximum likelihood method. The performance of the proposed
method is studied through simulation, and it is found that the biasness of the estimates
is small, thus implying the suitability of the method. Practical application of the
method is illustrated by using a real data set.
Recently, there is strong interest on the subject of outlier problem in circular data. In this paper, we focus on detecting outliers in a circular regression model proposed by Down and Mardia. The basic properties of the model are available including the exact form of covariance matrix of the parameters. Hence, we intend to identify outliers in the model by looking at the effect of the outliers on the covariance matrix. The method resembles closely the COVRATIO statistic for the case of linear regression problem. The corresponding critical values and the performance of the outlier detection procedure are studied via simulations. For illustration, we apply the procedure on the wind data set.
Missing value problem is common when analysing quantitative data. With the rapid growth of computing capabilities, advanced methods in particular those based on maximum likelihood estimation has been suggested to best handle the missing values problem. In this paper, two modern imputing approaches namely expectation-maximization (EM) and expectation-maximization with bootstrapping (EMB) are proposed in this paper for two kinds of linear functional relationship (LFRM) models, namely LFRM1 for full model and LFRM2 for linear functional relationship model when slope parameter is estimated using a nonparametric approach. The performance of EM and EMB are measured using mean absolute error, root-mean-square error and estimated bias. The results of the simulation study suggested that both EM and EMB methods are applicable to the LFRM with EMB algorithm outperforms the standard EM algorithm. Illustration using a practical example and a real data set is provided.
Multiple imputation method is a widely used method in missing data analysis. The method consists of a three-stage
process including imputation, analyzing and pooling. The number of imputations to be selected in the imputation step
in the first stage is important. Hence, this study aimed to examine the performance of multiple imputation method at
different numbers of imputations. Monotone missing data pattern was created in the study by deleting approximately 24%
of the observations from the continuous result variable with complete data. At the first stage of the multiple imputation
method, monotone regression imputation at different numbers of imputations (m=3, 5, 10 and 50) was performed. In the
second stage, parameter estimations and their standard errors were obtained by applying general linear model to each
of the complete data sets obtained. In the final stage, the obtained results were pooled and the effect of the numbers of
imputations on parameter estimations and their standard errors were evaluated on the basis of these results. In conclusion,
efficiency of parameter estimations at the number of imputation m=50 was determined as about 99%. Hence, at the
determined missing observation rate, increase was determined in efficiency and performance of the multiple imputation
method as the number of imputations increased.
In high-dimensional quantitative structure-activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.
Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
By using a linear operator, we obtain some new results for a normalized analytic function f defined by means of the Hadamard product of Hurwitz zeta function. A class related to this function will be introduced and the properties will be discussed.
Preliminary analysis of the short-term impact of a running headlights intervention revealed that there has been a significant drop in conspicuity-related motorcycle accidents in the pilot areas, Seremban and Shah Alam, Malaysia. This paper attempts to look in more detail at conspicuity-related accidents involving motorcycles. The aim of the analysis was to establish a statistical model to describe the relationship between the frequency of conspicuity-related motorcycle accidents and a range of explanatory variables so that new insights can be obtained into the effects of introducing a running headlight campaign and regulation. The exogenous variables in this analysis include the influence of time trends, changes in the recording and analysis system, the effect of fasting activities during Ramadhan and the "Balik Kampong" culture, a seasonal cultural-religious holiday activity unique to Malaysia. The model developed revealed that the running headlight intervention reduced the conspicuity-related motorcycle accidents by about 29%. It is concluded that the intervention has been successful in improving conspicuity-related motorcycle accidents in Malaysia.
Georgetown of Penang, an old city, is noted for its narrow streets. The existing traffic dispersal system is utterly inadequate to cope with the ever increasing number of cars and motorcycles on the road. The principal objective of this study is to build prediction models of CO to be employed as one of the planning tools in the future design of Penang urban traffic dispersal system. This study involves the monitoring of kerbside CO levels at selected sites and the fitting of hourly-averaged CO data to linear regression models incorporating the residual effect of CO emission due to traffic in the earlier periods and also different categories of vehicles. The best overall regression model appears to be the one based upon the total traffic count of motorcycles. This can be accounted for by the fact that the traffic counts of motorcycles and cars are highly correlated in most cases and that the emissions of CO from motorcycles are more readily detected as they travel closer to the kerb. The inclusion of residual CO in the models significantly improves the correlation coefficient from about 0.4 to about 0.7.
The effect of unmanageable construction waste is an unstable land settlement and groundwater pollution. In addition to environmental pollution, construction waste could incur construction cost. The most construction waste is the material used at sites and tile is also a part of the waste generated in construction. The objectives of this study are to determine the tile waste generated in construction stages and linear regression analysis for the amount of tile waste generated. The method used in this study was the Linear Regression Model. The regression model established in the sample data reported an R2 value of 0.793; therefore, the model can predict approximately 79.3% of the factor (area) of tile waste generation. The linear regressions can be applied as tools to predict the tile waste generated at construction sites and help the contractor to track the sources of missing waste.
Wind power is one of the most popular sources of renewable energies with an ideal extractable value that is limited to 0.593 known as the Betz-Joukowsky limit. As the generated power of wind machines is proportional to cubic wind speed, therefore it is logical that a small increment in wind speed will result in significant growth in generated power. Shrouding a wind turbine is an ordinary way to exceed the Betz limit, which accelerates the wind flow through the rotor plane. Several layouts of shrouds are developed by researchers. Recently an innovative controllable duct is developed by the authors of this work that can vary the shrouding angle, so its performance is different in each opening angle. As a wind tunnel investigation is heavily time-consuming and has a high cost, therefore just four different opening angles have been assessed. In this work, the performance of the turbine was predicted using multiple linear regression and an artificial neural network in a wide range of duct opening angles. For the turbine power generation and its rotor angular speed in different wind velocities and duct opening angles, regression and an ANN are suggested. The developed neural network model is found to possess better performance than the regression model for both turbine power curve and rotor speed estimation. This work revealed that in higher ranges of wind velocity, the turbine performance intensively will be a function of shrouding angle. This model can be used as a lookup table in controlling the turbines equipped with the proposed mechanism.
Agents are the most important marketing tools for company to become a successful in business. Agents not only operate as a channel to customers, but they also play an important role in providing customers with a variety of services before and after the sales. The main purpose of this study is to identify the factor influencing agent’s sales at an Apparel Manufacturing company. There are three categories of agents at the company namely, Trial agent, Basic agent and Premium agent. Based on the sales records in May 2018, the sales of product obtained by Trial Agent is lower than Basic and Premium Agents in this company. Therefore, this study aims to determine difference mean on record sales by agent among three categories of agents. This study also investigates the relationship between sales records by agents and years of experienced in business. Data was collected using questionnaire from 46 active agents at the company. Data was analyzed using One-way Analysis of Variance (ANOVA), Pearson correlation coefficient and Multiple Linear Regression. Result showed that there is a statistically significant difference in the mean sales records among the three of agent’s categories. Furthermore, there is a strong positive correlation between sales records by agent and years of experienced in business. Meanwhile, factors of knowledge and skills in business are most contributed to the agent’s sales. This study can help the company to create a strategic business plan and conducting several workshop trainings for agents to increase their knowledge and skills in business.
Lycopene and total phenolics of pink guava puree industry by-products (refiner, siever and decanter)
were evaluated after steam blanching at selected temperatures and times. Lycopene content was in the order of decanter > siever > refiner (7.3, 6.3 and 1.5 mg/100 g, respectively), and the content of total phenolics was in the order of refiner > siever > decanter (4434.1, 2881.3 and 1529.3 mg GAE/100 g, respectively). Regression coefficients for temperatures (x1) and times (x2) from multiple linear regression models of siever and decanter showed significant (p
The increase in car usage due to economic prosperity has led to increase in occupant injuries. One way to reduce the injuries encountered by road accident victims is by implementing the rear seatbelt (RSB) law. Rear seatbelt wearing has been proven to save lives. In Malaysia, the implementation of the restraint system for front occupant has started in the 70's. However, the rear seatbelt enforcement law only came in 2009, after six months of an advocacy program. Prior to the introduction of the rear seatbelt law, rear seatbelt wearing rate was rather low, started to increase gradually during the advocacy period and jumped to the highest level after two month of the enforcement. This paper attempts to assess the effectiveness of the rear seatbelt intervention in reducing injuries among passenger car occupants in Malaysia using the generalized linear model (GLM). In GLM procedure, the dependent variable is the number of people from passenger vehicles that sustained severe and slight injuries, for the study period. The study period selected covers six months before implementation, six months during advocacy program, and six months after the law is implemented. The independent variables considered are enforcement and balik kampung activities (both are dummy variables) and time effect. Our results suggest that RSB intervention (p-value= 0.0001) had significantly reduced the number of people sustained serious and slight injuries by about 20%. The implementation of change in the RSB law has benefited not only in reducing the number of injuries but also result to great impact to the health outcomes.
Monthly data about oil production at several drilling wells is an example of
spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal
model, i.e. Feedforward Neural Network - VectorAutoregressive (FFNN-VAR) and FFNN
- Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast
accuracy to linearspatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal
models are proposed and applied for forecasting monthly oil production data at three
drilling wells in East Java, Indonesia. There are 60 observations that be divided to two
parts, i.e. the first 50 observations for training data and the last 10 observations for
testing data. The results show that FFNN-GSTAR(11) and FFNN-VAR(1) as nonlinear
spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(11)
as linear spatio-temporal models. Moreover, further research about nonlinear spatiotemporal
models based on neural networks and GSTAR is needed for developing new
hybrid models that could improve the forecast accuracy.
Climate changes have become serious issues that have been widely discussed by researchers. One of the issues concerns with the study in changes of rainfall patterns. Changes in rainfall patterns affect the dryness and wetness conditions of a region. In this study, the three-dimensional loglinear model was used to fit the observed frequencies and to model the expected frequencies of wet class transition on eight rainfall stations in Peninsular Malaysia. The expected frequency values could be employed to determine the odds value of wet classes of each station. Further, the odds values were used to estimate the wet class of the following month if the wet class of the previous month and current month were identified. The wet classification based on SPI index (Standardized Precipitation Index). For station that was analyzed, there was no difference found were between estimated and observed wet classes. It was concluded that the loglinear models can be used to estimate the wetness classes through the estimates of odds values.