scholarly journals PERBANDINGAN REGRESI ROBUST METODE LEAST TRIMMED SQUARE (LTS) DAN METODE ESTIMASI-S PADA PRODUKSI PADI DI KABUPATEN BLITAR

2021 ◽  
Vol 10 (3) ◽  
pp. 329
Author(s):  
ENDAH SETYOWATI ◽  
RACHMADANIA AKBARITA ◽  
RIZKA RIZQI ROBBY
Keyword(s):  
Mean Square Error ◽  
Breakdown Point ◽  
Mean Square

Produksi padi di Kabupaten Blitar mengalami peningkatan dan penurunan, hal ini dipengaruhi oleh beberapa faktor, diantaranya jumlah petani, alokasi pupuk, ratarata curah hujan, luas panen, luas tanam, produktivitas, dan alat pengolah padi. Oleh karena itu, untuk mengetahui faktor-faktor yang lebih signifikan tersebut, guna mencapai produksi padi yang optimal dapat digunakan analisis regresi. Namun, adanya data pencilan pada suatu data penelitian dapat mengganggu proses analisis data. Regresi robust merupakan metode yang efisien untuk menganalisis data yang mengandung pencilan. Regresi robust memiliki beberapa metode estimasi, dua diantaranya adalah Least Trimmed Square (LTS) dan Estimasi S yang memiliki persamaan karateristik pada efisiensi dan breakdown point. Penelitian ini bertujuan untuk membandingkan kedua metode tersebut pada data produksi padi di Kabupaten Blitar tahun 2018 dengan tujuh variabel bebas (jumlah petani, alokasi pupuk, rata-rata curah hujan, luas panen, luas tanam, produktivitas, dan alat pengolah padi). Pengambilan data pada tahun 2018 didasarkan pada kelengkapan dokumen serta adanya kekhawatiran pandemi Covid-19 mempengaruhi data. Estimasi regresi robust menggunakan metode Least Trimmed Square (LTS) pada produksi padi di Kabupaten Blitar diperoleh model: Y = −11262, 756 − 0, 01x1 + 0, 031x2 − 14, 304x3 + 2, 292x4 + 3, 741x5 + 188, 274x6 − 0, 419x7 dan estimasi regresi robust menggunakan metode Estimasi S pada produksi padi di Kabupaten Blitar diperoleh model: Y = −9698, 949−0, 14x1−0, 49x2−19, 531x3+0, 133x4+5, 714x5+175, 018x6−0, 507x7. Hasil penelitian menunjukan regresi robust metode Least Trimmed Square (LTS) merupakan metode yang menghasilkan model terbaik, karena metode Least Trimmed Square (LTS) memiliki nilai koefisien determinasi (R2 ) sebesar 0, 99999 yang lebih besar dibandingkan nilai koefisien determinasi (R2 ) metode Estimasi S sebesar 0,99882, dan metode Least Trimmed Square (LTS) memiliki nilai Mean Square Error (MSE) sebesar 0,62105 yang lebih kecil dibandingkan nilai Mean Square Error (MSE) metode Estimasi S sebesar 9,04800.Kata Kunci: Data Pencilan (outlier), Produksi Padi, Regresi Robust

scholarly journals Perbandingan Penduga M, S, dan MM pada Regresi Linier dalam Menangani Keberadaan Outlier

2018 ◽  
Vol 15 (1) ◽  
pp. 88
Author(s):  
Hanifah Lainun ◽  
Georgina M Tinungki ◽  
Amran Amran
Keyword(s):  
Mean Square Error ◽  
Standard Error ◽  
Breakdown Point ◽  
Mean Square

Metode Kuadrat Terkecil (MKT) merupakan metode penduga parameter yang paling banyak digunakan pada analisis regresi. MKT merupakan metode penduga parameter tak bias yang baik selama asumsi komponen galatnya terpenuhi. Namun dalam aplikasinya sering ditemui terjadinya pelanggaran asumsi. Diantaranya, pelanggaran asumsi galat berdistribusi normal disebabkan adanya outlier pada data amatan. Oleh karena itu, dibutuhkan suatu metode yang kekar terhadap keberadaan outlier. Metode pendugaan parameter yang kekar terhadap keberadaan outlier pada regresi linier diantaranya ialah penduga  M, penduga S, dan penduga MM yang masing-masing memiliki keunggulan dari segi efisiensi dan breakdown point yang tinggi. Tujuan dari penelitian ini adalah untuk membandingkan penduga M, S, dan MM dalam menduga parameter regresi pada analisis regresi linier sederhana terhadap keberadaan outlier menggunakan data simulasi. Simulasi dilakukan untuk ukuran sampel yang berbeda (20, 60, dan 120) ketika terdapat 20% dan 45% outlier pada variabel bebas dan variabel terikat. Metode terbaik ialah metode dengan Standard Error (SE) dan Mean Square Error (MSE) terkecil. Hasil yang diperoleh menunjukkan bahwa penduga MM lebih baik dibandingkan penduga M dan penduga S.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling

2005 ◽  
Vol 10 (4) ◽  
pp. 333-342
Author(s):  
V. Chadyšas ◽  
D. Krapavickaitė
Keyword(s):  
Mean Square Error ◽  
Taylor Series ◽  
Random Sampling ◽  
Finite Population ◽  
Taylor Series Expansion ◽  
Simple Random Sampling ◽  
Population Parameter ◽  
Mean Square ◽  
Parameter Ratio ◽  
Using Data

Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.

Performance analysis of global local mean square error criterion of stochastic linearization for nonlinear oscillator

2019 ◽  
Author(s):  
Nguyen Cao Thang ◽  
Luu Xuan Hung
Keyword(s):  
Performance Analysis ◽  
Mean Square Error ◽  
Nonlinear Oscillators ◽  
Degree Of Freedom ◽  
Equivalent Linearization ◽  
Mean Square ◽  
Error Criterion ◽  
Stochastic Linearization ◽  
Local Mean ◽  
Mean Square Error Criterion

The paper presents a performance analysis of global-local mean square error criterion of stochastic linearization for some nonlinear oscillators. This criterion of stochastic linearization for nonlinear oscillators bases on dual conception to the local mean square error criterion (LOMSEC). The algorithm is generally built to multi degree of freedom (MDOF) nonlinear oscillators. Then, the performance analysis is carried out for two applications which comprise a rolling ship oscillation and two degree of freedom one. The improvement on accuracy of the proposed criterion has been shown in comparison with the conventional Gaussian equivalent linearization (GEL).

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

Performance enhancement of Echo Cancellation Using a Combination of Partial Update ( PU) Methods and New Variable Length LMS (NVLLMS) Algorithm

2018 ◽  
Vol 24 (5) ◽  
pp. 66
Author(s):  
Thamer M. Jamel ◽  
Faez Fawzi Hammood
Keyword(s):  
Mean Square Error ◽  
Return Loss ◽  
Performance Enhancement ◽  
Variable Length ◽  
Echo Cancellation ◽  
Convergence Time ◽  
Length Variation ◽  
Mean Square ◽  
Step Size ◽  
Echo Return Loss Enhancement

In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%, and 16% using Periodic, Sequential, Stochastic, and M-max PU NVLLMS algorithms respectively, compared to that used by a full update method which  is very important, especially in the application of mobile communication since the power consumption must be considered. In addition, the average ERLE and average Mean Square Error (MSE) for M-max PU NVLLMS are better than other proposed algorithms.  

Prediksi Daya Panel Surya Kapasitas 50 Wp Menggunakan Model Regresi Linier Majemuk

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Radian Indra Mukromin ◽  
Muhammad Khamim Asy'ari
Keyword(s):  
Mean Square Error ◽  
Mean Square

Sistem monitoring daya listrik pada panel surya penting dilakukan. Hal ini disebabkan daya listrik panel surya dapat mempengaruhi performansi pengisian baterai dan keandalan dari panel surya.  Sifat stokastik dari temperatur panel surya dan iradiasi surya mengakibatkan fluktuasi daya listrik, sehingga diperlukan sistem prediksi daya panel surya. Sistem prediksi dapat dirancang untuk mendapatkan model prediksi daya panel surya secara matematik menggunakan model regresi linier majemuk. Model dibangun untuk sistem prediksi dengan menggunakan data latih dari keluaran panel surya. Variasi yang diberikan adalah jenis variabel masukan untuk membangun model. Variabel masukan model terdiri dari temperatur panel surya, iradiasi surya, dan kombinasi dari keduanya. Pengujian data dilakukan dengan menggunakan uji korelasi majemuk, uji signifikasi regresi linier majemuk, dan uji signifikasi koefisien regresi. Hasil perancangan sistem prediksi terbaik adalah kombinasi temperatur panel surya dan iradiasi surya sebagai variabel masukan. Nilai MSE(mean square error) terkecil sebesar 9,83 untuk data latih dan 22,73 untuk data uji.

Cancelable Biometric System for face Based on linear minimum mean square error Model.

2019 ◽  
Vol 28 (1) ◽  
pp. 145-152
Author(s):  
Abd El-aziz Ebrahim Hsaneen ◽  
EL-Sayed M. El-Rabaei ◽  
Moawad I. Dessouky ◽  
Ghada El-bamby ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Error Model ◽  
Mean Square ◽  
Biometric System

Limit tolerances for sums of measurement errors

2018 ◽  
Vol 934 (4) ◽  
pp. 59-62
Author(s):  
V.I. Salnikov
Keyword(s):  
Normal Distribution ◽  
Mean Square Error ◽  
Gaussian Distribution ◽  
Measurement Errors ◽  
Theory And Practice ◽  
Mean Square ◽  
The Mean ◽  
Limiting Values ◽  
Limiting Value

The question of calculating the limiting values of residuals in geodesic constructions is considered in the case when the limiting value for measurement errors is assumed equal to 3m, ie ∆рred = 3m, where m is the mean square error of the measurement. Larger errors are rejected. At present, the limiting value for the residual is calculated by the formula 3m√n, where n is the number of measurements. The article draws attention to two contradictions between theory and practice arising from the use of this formula. First, the formula is derived from the classical law of the normal Gaussian distribution, and it is applied to the truncated law of the normal distribution. And, secondly, as shown in [1], when ∆рred = 2m, the sums of errors naturally take the value equal to ?pred, after which the number of errors in the sum starts anew. This article establishes its validity for ∆рred = 3m. A table of comparative values of the tolerances valid and recommended for more stringent ones is given. The article gives a graph of applied and recommended tolerances for ∆рred = 3m.

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