scholarly journals Stationarity of Regression Relationships: Application to Empirical Downscaling

2008 ◽  
Vol 21 (17) ◽  
pp. 4529-4537 ◽  
Author(s):  
Torben Schmith
Keyword(s):  
Monte Carlo ◽  
Surface Temperature ◽  
Regression Model ◽  
Statistical Downscaling ◽  
Large Fraction ◽  
Statistical Downscaling Model ◽  
Significance Tests ◽  
Simple Regression Model ◽  
Empirical Downscaling ◽  
Simple Regression

Abstract The performance of a statistical downscaling model is usually evaluated for its ability to explain a large fraction of predictand variance. In this note, it is shown that although this fraction may be high, the longest time scales, including trends, may not be explained by the model. This implies that the model is nonstationary over the training period of the model, and it questions the basic stationarity assumption of statistical downscaling. This is exemplified by using a simple regression model for downscaling European precipitation and surface temperature where appropriate Monte Carlo–based field significance tests are developed, taking into account the intercorrelation between predictand series. Based on this test, it is concluded that care is needed in selecting predictors to avoid this form of nonstationarity. Even though this is illustrated for a simple regression-type statistical downscaling model, the main conclusions may also be valid for more complicated models.

Face Gear Width Prediction Using the DOE Method

2008 ◽  
Vol 130 (10) ◽  
Author(s):  
Michèle Guingand ◽  
Didier Remond ◽  
Jean-Pierre de Vaujany
Keyword(s):  
Regression Model ◽  
Simple Formula ◽  
Design Parameters ◽  
Face Gear ◽  
Main Design ◽  
Gear Design ◽  
Simple Regression Model ◽  
Numerical Tool ◽  
Influential Parameters ◽  
Simple Regression

This paper deals with face gear design. The goal is to propose a simple formula for predicting the width of the wheel as a function of the main design parameters. A specific software was used to achieve this goal. This numerical tool is able to simulate the geometry and the quasistatic loaded behavior of a face gear. The statistical method used for analyzing the influence of data is described: The design of experiments leads to a simple regression model taking into account the influential parameters and their couplings. In the last part of this paper, the results of the formulas are compared to those of the software and an optimal design is proposed based on the regression model.

scholarly journals PENGARUH TATA KELOLA PEMERINTAH DAERAH TERHADAPTRANSPARANSI PENGELOLAAN KEUANGAN DAERAH: BUKTI EMPIRIS PADA PEMERINTAH PROVINSI DI INDONESIA

2017 ◽  
Vol 21 (3) ◽  
pp. 448
Author(s):  
Syamsul Syamsul ◽  
Irwan Taufiq Ritonga
Keyword(s):  
Corporate Governance ◽  
Regression Model ◽  
Financial Management ◽  
Local Governments ◽  
Environmental Governance ◽  
Local Governance ◽  
Government Officials ◽  
Simple Regression Model ◽  
Simple Regression

This study developed a research Beekes and Brown (2006) who found that corporate governance makes companies more informative (more transparent). This study aims to prove whether the same results were also found in environmental governance in Indonesia. The theory is used to achieve the goal of this research is the theory of agency. This research was conducted in 32 local governments in Indonesia. Based on a simple regression model, this study shows that local governance affects positively the transparency of local financial management. Such findings reinforce previous research. The findings of this study provide a useful contribution to government officials (executive and legislative), in demonstrating the important role of local governance in encouraging the transparency of local financial management. In addition, the findings of this study can be used as the basis for further research related to the topic of local governance and transparency of local financial management.

PENGARUH LABEL HALAL TERHADAP KEPUTUSAN MASYARAKAT MEMBELI PRODUK MAKANAN DAN MINUMAN (Studi Kasus Lingkungan VI Kelurahan Nangka Binjai Utara)

2020 ◽  
Vol 5 (2) ◽  
pp. 354
Author(s):  
Raja Sakti Putra Harahap
Keyword(s):  
Regression Model ◽  
Quantitative Method ◽  
Statistical Tests ◽  
A Value ◽  
Regression Formula ◽  
Simple Regression Model ◽  
Food And Beverage ◽  
R Value ◽  
Positive Effect ◽  
Simple Regression

This study aims to determine how the effect of the halal label on people’s decisions to buy food and beverage products. The method used is a quantitative method with a simple regression model and using statistical tests with the help of IBM SPSS Statistics 22 for windows. The sample in this study is the neighborhood community VI Nangka Village as many as 70 respondents. The results showed that the calculated r value was 0,79, so it could be saidthat there was s relationship or correlation between the variables X (Halal Label) with the variable Y ( The decision to buy food and beverage products). Then the t value < t table, which has a value of 0,657 < 1,668. Then  is accepted and  is rejected, which means that partially (X) variable does not have a significant effect on variable (Y), where the results of the hypothesis are accepted and proven after being calculated using a simple regression formula, namely Y = 34,7 + 0,67X.  By having a regression coefficoent of 0,675%, so the halal label has a positive effect on decisions to buy food and beverage products.

scholarly journals An Evaluation of Alternative Multiple Testing Methods for Finance Applications*

2020 ◽  
Vol 10 (2) ◽  
pp. 199-248 ◽  
Author(s):  
Campbell R Harvey ◽  
Yan Liu ◽  
Alessio Saretto
Keyword(s):  
Regression Model ◽  
Multiple Testing ◽  
Empirical Finance ◽  
Relative Performance ◽  
Testing Methods ◽  
Multiple Tests ◽  
High Profile ◽  
Simple Regression Model ◽  
Testing Environments ◽  
Simple Regression

Abstract In almost every area of empirical finance, researchers confront multiple tests. One high-profile example is the identification of outperforming investment managers, many of whom beat their benchmarks purely by luck. Multiple testing methods are designed to control for luck. Factor selection is another glaring case in which multiple tests are performed, but numerous other applications do not receive as much attention. One important example is a simple regression model testing five variables. In this case, because five variables are tried, a t-statistic of 2.0 is not enough to establish significance. Our paper provides a guide to various multiple testing methods and details a number of applications. We provide simulation evidence on the relative performance of different methods across a variety of testing environments. The goal of our paper is to provide a menu that researchers can choose from to improve inference in financial economics. (JEL G0, G1, G3, G5, M4, C1)

Prediction of hydraulic load for urban storm control of a municipal WWT plant

1998 ◽  
Vol 37 (12) ◽  
pp. 363-370 ◽  
Author(s):  
Jacob Carstensen ◽  
Marinus K. Nielsen ◽  
Helle Strandbæk
Keyword(s):  
Regression Model ◽  
Flow Model ◽  
Measured Data ◽  
Box Model ◽  
Significant Difference ◽  
Simple Regression Model ◽  
Hydraulic Load ◽  
Dynamical Flow ◽  
Simple Regression ◽  
Better Than

Three different methodologies are assessed which provide predictions of the hydraulic load to the treatment plant one hour ahead. The three models represent three different levels of complexity ranging from a simple regression model over an adaptive grey-box model to a complex hydrological and full dynamical wave model. The simple regression model is estimated as a transfer function model of rainfall intensity to influent flow. It also provides a model for the base flow. The grey-box model is a state space model which incorporates adaptation to the dry weather flow as well as the rainfall runoff. The full dynamical flow model is a distributed deterministic model with many parameters, which has been calibrated based on extensive measurement campaigns in the sewer system. The three models are compared by the ability to predict the hydraulic load one hour ahead. Five rain events in a test period are used for evaluating the three different methods. The predictions are compared to the actual measured flow at the plant one hour later. The results show that the simple regression model and the adaptive grey-box model which are identified and estimated on measured data perform significantly better than the hydrological and full dynamical flow model which is not identifiable and needs calibration by hand. For frontal rains no significant difference in the prediction performance between the simple regression model and the adaptive grey-box model is observed. This is due to a rather uniform distribution of frontal rains. A single convective rain justifies the adaptivity of the grey-box model for non-uniformly distributed rain, i.e. the predictions of the grey-box model were significantly better than the predictions of the simple regression model for this rain event. In general, models for model-based predictive control should be kept simple and identifiable from measured data.

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