scholarly journals Functional SAC model: With application to spatial econometrics

2021 ◽  
Vol 55 (1) ◽  
pp. 1-13
Author(s):  
Alassane Aw ◽  
Emmanuel N. Cabral
Keyword(s):  
Maximum Likelihood ◽  
Spatial Econometrics ◽  
Spatial Data ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Explanatory Variables ◽  
Dependent Variables ◽  
Spatial Autoregressive ◽  
The Relationship ◽  
Maximum Likelihood Estimation Method

Spatial autoregressive combined (SAC) models have been widely studied in the literature for the analysis of spatial data in various areas such as geography, economics, demography, regional sciences. This is a linear model with scalar response and scalar explanatory variables which allows for spatial interactions in the dependent variables and the disturbances. In this work we extend this modeling approach from scalar to functional covariate. The parameters of the model are estimated via the maximum likelihood estimation method. A simulation study is conducted to evaluate the performance of the proposed methodology. As an illustration, the model is used to establish the relationship between unemployment and illiteracy in Senegal.

scholarly journals PEMODELAN TINGKAT KERAWANAN DEMAM BERDARAH DI KABUPATEN BANJAR DENGAN METODE ANALISIS REGRESI LOGISTIK YANG TERBOBOTI GEOGRAFIS

2019 ◽  
Vol 14 (4) ◽  
pp. 2393
Author(s):  
Dewi Sri Susanti ◽  
Pamona Dwi Rahayu ◽  
Oni Soesanto
Keyword(s):  
Logistic Regression ◽  
Dengue Fever ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Rate Model ◽  
Independent Variables ◽  
Dependent Variables ◽  
Geographically Weighted Logistic Regression ◽  
The Relationship ◽  
Maximum Likelihood Estimation Method

Regression analysis is a metodh for investigating the relationship between the dependent variable (Y) and independent variables (X). Logistic regression is a regression model that used related to the qualitative Dependent variable. If the Logistic regression influenced by factors of the location of each point from observation where the data is collected, it will be a Geographically Weighted Logistic Regression (GWLR). In the case of insecurity rate model of dengue fever has two or more categories, so that this case can be resolved by GWLR. This research aims to clarify the procedure of testing the parameters GWLR model and form insecurity rate model of dengue fever with GWLR method in Banjar Regency. Dependent variable with catagoric is Insecurity rate of dengue fever ( ) and independent variables is the population density ( ), the distance from the capital of the subdistrict to capital of regency ( ), fogging per subdistrict ( ), the percentage of households living clean and healthy ( ), pesentase healthy homes ( ), the percentage of access to decent sanitation ( ). The results from this research are estimate parameters using Maximum Likelihood Estimation method and presented in the form of thematic map that shows not all dependent variables give influence on Insecurity rate dengue fever

scholarly journals A Bi-Level Weibull Model with Applications to Two Ordered Events

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Shuguang Song ◽  
Hanlin Liu ◽  
Mimi Zhang ◽  
Min Xie
Keyword(s):  
Maximum Likelihood ◽  
Estimation Method ◽  
Mixture Distribution ◽  
Likelihood Estimation ◽  
Random Variable ◽  
Weibull Model ◽  
Reliability Function ◽  
The Other ◽  
Regularity Conditions ◽  
Maximum Likelihood Estimation Method

In this paper, we propose and study a new bivariate Weibull model, called Bi-levelWeibullModel, which arises when one failure occurs after the other. Under some specific regularity conditions, the reliability function of the second event can be above the reliability function of the first event, and is always above the reliability function of the transformed first event, which is a univariate Weibull random variable. This model is motivated by a common physical feature that arises fromseveral real applications. The two marginal distributions are a Weibull distribution and a generalized three-parameter Weibull mixture distribution. Some useful properties of the model are derived, and we also present the maximum likelihood estimation method. A real example is provided to illustrate the application of the model.

scholarly journals Detecting long-memory: Monte Carlo simulations and application to daily streamflow processes

2006 ◽  
Vol 3 (4) ◽  
pp. 1603-1627 ◽  
Author(s):  
W. Wang ◽  
P. H. A. J. M. van Gelder ◽  
J. K. Vrijling ◽  
X. Chen
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Maximum Likelihood ◽  
Long Memory ◽  
Positive Relationship ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Large Data ◽  
Daily Streamflow ◽  
Maximum Likelihood Estimation Method

Abstract. The Lo's R/S tests (Lo, 1991), GPH test (Geweke and Porter-Hudak, 1983) and the maximum likelihood estimation method implemented in S-Plus (S-MLE) are evaluated through intensive Mote Carlo simulations for detecting the existence of long-memory. It is shown that, it is difficult to find an appropriate lag q for Lo's test for different AR and ARFIMA processes, which makes the use of Lo's test very tricky. In general, the GPH test outperforms the Lo's test, but for cases where there is strong autocorrelations (e.g., AR(1) processes with φ=0.97 or even 0.99), the GPH test is totally useless, even for time series of large data size. Although S-MLE method does not provide a statistic test for the existence of long-memory, the estimates of d given by S-MLE seems to give a good indication of whether or not the long-memory is present. Data size has a significant impact on the power of all the three methods. Generally, the power of Lo's test and GPH test increases with the increase of data size, and the estimates of d with GPH test and S-MLE converge with the increase of data size. According to the results with the Lo's R/S test (Lo, 1991), GPH test (Geweke and Porter-Hudak, 1983) and the S-MLE method, all daily flow series exhibit long-memory. The intensity of long-memory in daily streamflow processes has only a very weak positive relationship with the scale of watershed.

scholarly journals A Stochastic Model of Stress Evolution in a Bolted Structure in the Presence of a Joint Elastic Piece: Modeling and Parameter Inference

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mohammed Haiek ◽  
Youness El Ansari ◽  
Nabil Ben Said Amrani ◽  
Driss Sarsri
Keyword(s):  
Numerical Simulation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Stochastic Model ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Stress Evolution ◽  
Parameter Inference ◽  
Maximum Likelihood Estimation Method ◽  
Over Time

In this paper, we propose a stochastic model to describe over time the evolution of stress in a bolted mechanical structure depending on different thicknesses of a joint elastic piece. First, the studied structure and the experiment numerical simulation are presented. Next, we validate statistically our proposed stochastic model, and we use the maximum likelihood estimation method based on Euler–Maruyama scheme to estimate the parameters of this model. Thereafter, we use the estimated model to compare the stresses, the peak times, and extinction times for different thicknesses of the elastic piece. Some numerical simulations are carried out to illustrate different results.

scholarly journals Calibration of MEMS Triaxial Accelerometers Based on the Maximum Likelihood Estimation Method

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yifan Sun ◽  
Xiang Xu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Random Noise ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Least Square ◽  
Triaxial Accelerometer ◽  
Zero Bias ◽  
Estimation Function ◽  
Maximum Likelihood Estimation Method

As a widely used inertial device, a MEMS triaxial accelerometer has zero-bias error, nonorthogonal error, and scale-factor error due to technical defects. Raw readings without calibration might seriously affect the accuracy of inertial navigation system. Therefore, it is necessary to conduct calibration processing before using a MEMS triaxial accelerometer. This paper presents a MEMS triaxial accelerometer calibration method based on the maximum likelihood estimation method. The error of the MEMS triaxial accelerometer comes into question, and the optimal estimation function is established. The calibration parameters are obtained by the Newton iteration method, which is more efficient and accurate. Compared with the least square method, which estimates the parameters of the suboptimal estimation function established under the condition of assuming that the mean of the random noise is zero, the parameters calibrated by the maximum likelihood estimation method are more accurate and stable. Moreover, the proposed method has low computation, which is more functional. Simulation and experimental results using the consumer low-cost MEMS triaxial accelerometer are presented to support the abovementioned superiorities of the maximum likelihood estimation method. The proposed method has the potential to be applied to other triaxial inertial sensors.

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