Analyzing The Effect of BI 7-Days Repo Rate on The Jakarta Composite Index Using Nonparametric Regression Approaches Based on Least Square Spline Estimator

During the Covid-19 pandemic, the Indonesia stock market was under great pressure, so that the value of the Jakarta Composite Index (JCI) fluctuated greatly. To maintain economic stability, Bank Indonesia has regulated monetary policy such as setting the BI 7-Days Repo Rate. Analysis of this effect is important to formulate the right policy. This study aims to design the best model in describing the relationship between JCI value and BI 7-Days Repo Rate. The analysis was carried out by using parametric regression approach based on the ordinary least square method and nonparametric regression approach based on least square spline estimator. The results showed that the parametric regression models failed to meet the classical assumptions. Meanwhile, nonparametric regression can produce an optimal model with high accurate prediction, with an overall mean absolute percentage error value of 3.16%. Furthermore, mean square error, coefficient of determination, and mean absolute deviation also show good results. Thus, the effect of the BI 7-Days Repo Rate on the JCI value forms a quadratic pattern, in which a positive relationship is formed when the BI 7-Days Repo Rate is set at more than 4.25% and vice versa for a negative relationship.

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Truncated Spline Estimation of Percentage Poverty Modeling in Papua Province

ICSA - International Conference on Statistics and Analytics 2019 ◽

10.29244/icsa.2019.pp69-82 ◽

2021 ◽

pp. 69-82

Author(s):

Ni Putu Ayu Mirah Mariati ◽

Nyoman Budiantara ◽

Vita Ratnasari

Keyword(s):

Nonparametric Regression ◽

Semiparametric Regression ◽

Poor People ◽

Regression Curve ◽

Parametric Regression ◽

Regression Approach ◽

Spline Estimation ◽

High Flexibility

In estimating the regression curve there are three approaches, namely parametric regression, nonparametric regression and semiparametric regression. Nonparametric regression approach has high flexibility. Nonparametric regression approach that is quite popular is Truncated Spline. Truncated Spline is a polynomial pieces which have segmented and continuous. One of the advantages of Spline is that it can handle data that changes at certain sub intervals, so this model tends to search for data estimates wherever the data pattern moves and there are points of knots. In reality, data patterns often change at certain sub intervals, one of which is data on poverty in the Papua Province. Papua Province is ranked first in the percentage of poor people in Indonesia. The best of model Truncated Spline in nonparametric regression for the poverty model in Papua Province is using a combination of knot.

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Determinan Jumlah Uang Beredar, Tingkat Bunga, dan Inflasi Terhadap Pertumbuhan Ekonomi Indonesia

Al-Buhuts ◽

10.30603/ab.v15i2.1106 ◽

2019 ◽

Vol 15 (2) ◽

pp. 45-64

Author(s):

Adya Utami

Keyword(s):

Economic Growth ◽

Interest Rate ◽

Money Supply ◽

Negative Relationship ◽

Secondary Data ◽

Least Square Method ◽

Least Square ◽

Coefficient Of Determination ◽

Independent Variable ◽

The Interest Rate

This study aims to determine the determinants of the money supply, the interest rate, and inflation on Indonesia's economic growth in the 2009-2018 period. This research uses descriptive method and is strengthened by the OLS (ordinary least square) method with secondary data. The data used is sourced from the Central Statistics Agency and Bank Indonesia. The results of this study indicate that the money supply and the interest rate have a negative effect but inflation has a positive effect on Indonesia's economic growth. The JUB variable is not significant with a probability value of 0.1326. The JUB regression coefficient value has a negative relationship to the economic growth variable with a coefficient of 0.9288. The interest rate variable entered in the above equation turns out to be negative and significant with a probability value of 0.0571. The value of the coefficient of the exchange rate is (0.4843). The independent variable inflation gives a negative and not significant result with a probability value of 0.1134. Inflation coefficient value is 0.1724. In the equation model that uses economic growth as the dependent variable above, the magnitude of the coefficient of determination (R Squared) is 0.573429. This shows that the ability of the independent variable in explaining the diversity of the independent variables is 57.34% while the remaining 42.66% is influenced by other variables not included in the model.

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Growth models for morphological traits of sunn hemp

Semina Ciências Agrárias ◽

10.5433/1679-0359.2017v38n5p2933 ◽

2017 ◽

Vol 38 (5) ◽

pp. 2933

Author(s):

Cláudia Marques de Bem ◽

Alberto Cargnelutti Filho ◽

Giovani Facco ◽

Denison Esequiel Schabarum ◽

Daniela Lixinski Silveira ◽

...

Keyword(s):

Morphological Traits ◽

Logistic Models ◽

Information Criterion ◽

Coefficient Of Determination ◽

Percentage Error ◽

Mean Absolute Deviation ◽

Absolute Deviation ◽

Sunn Hemp ◽

Number Of Leaves ◽

Second Period

The objective of the present study was to fit Gompertz and Logistic nonlinear to descriptions of morphological traits of sunn hemp. Two uniformity trials were conducted and the crops received identical treatment in all experimental area. Sunn hemp seeds were sown in rows 0.5 m apart with a plant density of 20 plants per row meter in a usable area of 52 m × 50 m. The following morphological traits were evaluated: plant height (PH), number of leaves (NL), stem diameter (SD), and root length (RL). These traits were assessed daily during two sowing periods—seeds were sown on October 22, 2014 (first period) and December 3, 2014 (second period). Four plants were randomly collected daily, beginning 7 days after first period and 13 days after for second period, totaling 94 and 76 evaluation days, respectively. For Gompertz models the equation was used y=a*e^((?-e?^((b-c*xi))and Logistic models the equation was used yi= a/(1+e^((-b-c*xi)). The inflection points of the Gompertz and Logistic models were calculated and the goodness of fit was quantified using the adjusted coefficient of determination, Akaike information criterion, standard deviation of residuals, mean absolute deviation, mean absolute percentage error, and mean prediction error. Differences were observed between the Gompertz and Logistic models and between the experimental periods in the parameter estimate for all morphological traits measured. Satisfactory growth curve fittings were achieved for plant height, number of leaves, and stem diameter in both models using the evaluation criteria: coefficient of determination (R²), Akaike information criterion (AIC), standard deviation of residuals (SDR), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and mean prediction error (MPE).

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Estimation of the Number of Cumulative COVID-19 Cases by Day in Thailand Based on a Flattened Curve Policy

Walailak Journal of Science and Technology (WJST) ◽

10.48048/wjst.2021.10054 ◽

2021 ◽

Vol 18 (13) ◽

Author(s):

Yupaporn AREEPONG ◽

Rapin SUNTHORNWAT

Keyword(s):

Growth Curve ◽

Exponential Growth ◽

Herd Immunity ◽

Growth Curves ◽

Least Square Method ◽

Least Square ◽

Coefficient Of Determination ◽

Percentage Error ◽

Logistic Growth ◽

Thai Government

Since December 2019, the world has been facing an emerging infectious disease named coronavirus disease 2019. Thailand has also been affected by the spread of the coronavirus. The Thai government have announced policies to protect people, based on the emergency decree and curfew law for flattening the curve of the number of the coronavirus disease 2019 cases without vaccination in Thailand. This research estimated of the number of total infectious cases of coronavirus disease 2019 in Thailand. Two growth curves, including an exponential growth curve under a non-flattened curve policy (herd immunity policy without vaccination), and a logistic growth curve under a flattened curve policy without vaccination, were selected to estimate the parameters of the curves by the least square method to represent the number of the total infectious cases in Thailand. Moreover, the maximum infectious cases of coronavirus disease 2019 and the speed of spreading for coronavirus disease 2019 in Thailand were also explored. Based on the number of the total infectious cases of coronavirus disease 2019 in Thailand, the findings demonstrated that the coefficient of determination of the logistic growth curve was greater than the exponential growth curve and the root means squared percentage error of the logistic growth curve was less than the exponential growth curve. These results suggest that the logistic growth curve is suitable for describing the number of total infectious cases of coronavirus disease 2019 in Thailand under the fattened curve policy. GRAPHICAL ABSTRACT

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PENGUJIAN HIPOTESIS SIMULTAN MODEL REGRESI NONPARAMETRIK SPLINE TRUNCATED DALAM PEMODELAN KASUS EKONOMI

Jambura Journal of Probability and Statistics ◽

10.34312/jjps.v1i2.7755 ◽

2020 ◽

Vol 1 (2) ◽

pp. 98-106

Author(s):

ANDREA TRI RIAN DANI ◽

NARITA YURI ADRIANINGSIH ◽

ALIFTA AINURROCHMAH

Keyword(s):

Nonparametric Regression ◽

Predictor Variable ◽

Coefficient Of Determination ◽

Poor People ◽

Nonparametric Regression Model ◽

Response Variable ◽

Simultaneous Testing ◽

Regression Approach ◽

Best Estimator

The pattern in a relationship between the response variable and the predictor variable can be known and some cannot be known. In determining the unknown pattern of relationships, nonparametric regression approaches can be used. The nonparametric regression approach is very flexible. One of the most frequently used nonparametric regression approaches is the truncated spline. Truncated splines are polynomial pieces that are segmented and continuous. The purpose of this study is to obtain the best estimator model in the Gini Ratio case against the variables suspected of influencing it, then perform simultaneous hypothesis testing on the nonparametric regression model. The criteria for the goodness of the model use the GCV and R2 values. In the case modeling of the District / City Gini Ratio in East Java Province using a nonparametric regression approach, it was found that the truncated spline estimator with 3 knots points gave quite good results. This is indicated by the coefficient of determination of the truncated spline estimator, which is 84.76%. Based on the results of simultaneous testing, it was found that the open unemployment rate, the percentage of poor people and the rate of economic growth simultaneously had an influence on the Gini Ratio.

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Development of a prediction model based on linear regression to estimate the success rates of seafood caught from different catching centers

International Journal of ADVANCED AND APPLIED SCIENCES ◽

10.21833/ijaas.2021.12.013 ◽

2021 ◽

Vol 8 (12) ◽

pp. 110-116

Author(s):

Sherimon et al. ◽

Keyword(s):

Mean Square Error ◽

Least Square ◽

Percentage Error ◽

Mean Square ◽

Success Rates ◽

Total Quality ◽

Absolute Deviation ◽

Safety Standards ◽

Intelligent Software ◽

Seafood Processing

For businesses and organizations that aim to be efficient and competitive on a worldwide basis, food quality assurance is extremely important. To maintain constant quality, global markets demand high food hygiene and safety standards. Intelligent software to assure fish quality is uncommon in the fishing industry. Most seafood processing industries utilize Total Quality Management (TQM) systems to ensure product safety and quality. These protections ensure that significant quality risks are kept within acceptable tolerance limits. However, there are no ways for calculating the success rates of seafood obtained from different catching centers. The purpose of this study is to develop algorithms for predicting the success rates of seafood caught at different catching centers. To determine the best model to match the data, the algorithms employ the Least-Square Curve Fitting approach. The success rates are predicted using the best-fit model that results. The bestFitModelFinder algorithm is used to find the best model for the input data, while the prediction of quality algorithm is used to predict the success rate. The algorithms were tested using data obtained from a seafood company between January 2000 and December 2019. Statistical metrics such as mean absolute deviation (MAD), mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to evaluate the prediction accuracy of the presented algorithms. The algorithms' performance analysis resulted in lower error levels. The proposed algorithms can assist seafood enterprises in determining the quality of seafood items sourced from various fishing areas.

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Comparative Evaluation of Artificial Intelligence Models for Drilling Rate of Penetration Prediction

10.2118/208451-ms ◽

2021 ◽

Author(s):

Ololade Adetifa ◽

Ibiye Iyalla ◽

Kingsley Amadi

Keyword(s):

Artificial Intelligence ◽

Least Square ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Artificial Intelligent ◽

Rate Of Penetration ◽

Drilling Data ◽

Intelligent Models ◽

Prediction Capability

Abstract Rate of penetration is an important parameter in drilling performance analysis. The accurate prediction of rate of penetration during well planning leads to a reduction in capital and operating costs which is vital given the recent downturn in oil prices. The industry is seen to embrace the use of novel technologies and artificial intelligence in its bid to be sustainable which is why this study focuses on the use of artificial intelligent models in predicting the rate of penetration. The predictive performance of three data-driven models [artificial neural network (ANN), extreme learning machine (ELM) and least-square support vector machine (LS-SVM)] were evaluated using actual drilling data based on three performance evaluation criteria [mean square error (MSE), coefficient of determination (R2) and average absolute percentage error (AAPE)]. The models were validated using the physics based Bourgoyne and Young's model. The results show that all three models performed to an acceptable level of accuracy based on the range of the actual drilling data because, although the ELM had the least MSE (1317.44) and the highest R2 (0.52 i.e. 52% prediction capability) the LS-SVM model had a smaller spread of predicted ROP when compared with the actual ROP and the ANN had the least AAPE (38.14). The results can be improved upon by optimizing the controllable predictors. Validation of the model's performance with the Bourgoyne and Young's model resulted in R2 of 0.29 or 29% prediction capability confirming that artificial intelligent models outperformed the physics-based model.

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PEMODELAN REGRESI SEMIPARAMETRIK DENGAN PENDEKATAN DERET FOURIER (Studi Kasus: Pengaruh Indeks Dow Jones dan BI Rate Terhadap Indeks Harga Saham Gabungan

Jurnal Gaussian ◽

10.14710/j.gauss.v9i1.27523 ◽

2020 ◽

Vol 9 (1) ◽

pp. 50-63

Author(s):

Laili Rahma Khairunnisa ◽

Alan Prahutama ◽

Rukun Santoso

Keyword(s):

Fourier Series ◽

Stock Price ◽

Price Index ◽

Semiparametric Regression ◽

Least Square ◽

Coefficient Of Determination ◽

Percentage Error ◽

K Value ◽

Sample Data ◽

Stock Price Index

The Composite Stock Price Index (CSPI) is a composite index all of types of shares listed on the stock exchange and their movements indicate conditions that occur in the capital market. CSPI is influenced by macroeconomic factors and foreign exchange index. Dow Jones Industrial Average has a linear relationship with CSPI and BI Rate has a repeated relationship with CSPI, so the method is used semiparametric regression with the Fourier series approach. Estimators in semiparametric regression with Fourier series approach were obtained by the Ordinary Least Square (OLS) method. This study uses monthly data which is divided into in sample data and out sample data. Semiparametric regression modelling with Fourier series approach is done by determining the optimal K value which results in a minimum General Cross Validation (GCV) value. In this study, semiparametric regression model with Fourier series approach formed by the optimal K value is 13 and GCV is 2826122. The results of the evaluation of the accuracy of the model performance and forecasting obtained the coefficient of determination is 0,9226, Mean Absolute Percentage Error (MAPE) data in sample 3,8154% and data out sample is 8,4782% which shows that the model obtained has a very accurate performance.Keywords: Composite Stock Price Index (CSPI), Semiparametric Regression, Fourier Series, OLS, GCV

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Influence of Operating Parameters on Running-in Wear of EN-31 Steel

Technological Engineering ◽

10.2478/teen-2016-0001 ◽

2016 ◽

Vol 13 (1) ◽

pp. 1-2

Author(s):

M. Hanief ◽

M. F. Wani

Keyword(s):

Surface Roughness ◽

Wear Rate ◽

Least Square Method ◽

Least Square ◽

Coefficient Of Determination ◽

Percentage Error ◽

Model Parameters ◽

Operating Parameters ◽

Proposed Model ◽

En 31

Abstract In this paper, effect of operating parameters (temperature, surface roughness and load) was investigated to determine the influence of each parameter on the wear rate. A mathematical model was developed to establish a functional relationship between the running-in wear rate and the operating parameters. The proposed model being non-linear, it was linearized by logarithmic transformation and the optimal values of model parameters were obtained by least square method. It was found that the surface roughness has significant effect on wear rate followed by load and temperature. The adequacy of the model was estimated by statistical methods (coefficient of determination (R2) and mean absolute percentage error (MAPE)) .

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The Curve Estimation of Combined Truncated Spline and Fourier Series Estimators for Multiresponse Nonparametric Regression

Mathematics ◽

10.3390/math9101141 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1141

Author(s):

Helida Nurcahayani ◽

I Nyoman Budiantara ◽

Ismaini Zain

Keyword(s):

Fourier Series ◽

Nonparametric Regression ◽

Estimation Method ◽

Size Variation ◽

Least Square ◽

Coefficient Of Determination ◽

Regression Curve ◽

Curve Estimation ◽

Weighted Least Square ◽

Proposed Model

Nonparametric regression becomes a potential solution if the parametric regression assumption is too restrictive while the regression curve is assumed to be known. In multivariable nonparametric regression, the pattern of each predictor variable’s relationship with the response variable is not always the same; thus, a combined estimator is recommended. In addition, regression modeling sometimes involves more than one response, i.e., multiresponse situations. Therefore, we propose a new estimation method of performing multiresponse nonparametric regression with a combined estimator. The objective is to estimate the regression curve using combined truncated spline and Fourier series estimators for multiresponse nonparametric regression. The regression curve estimation of the proposed model is obtained via two-stage estimation: (1) penalized weighted least square and (2) weighted least square. Simulation data with sample size variation and different error variance were applied, where the best model satisfied the result through a large sample with small variance. Additionally, the application of the regression curve estimation to a real dataset of human development index indicators in East Java Province, Indonesia, showed that the proposed model had better performance than uncombined estimators. Moreover, an adequate coefficient of determination of the best model indicated that the proposed model successfully explained the data variation.

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