scholarly journals From regression function to diffusion drift estimation in nonparametric setting

2020 ◽  
Vol 68 ◽  
pp. 20-34
Author(s):  
Fabienne Comte
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Diffusion Model ◽  
Regression Function ◽  
Nonparametric Estimators ◽  
Process Solution ◽  
Drift Estimation ◽  
Heteroscedastic Model ◽  
Projection Estimators

We consider a diffusion model dXt = b(Xt)dt + σ(Xt)dWt,X0 = η, under conditions ensuring existence, stationarity and geometrical β-mixing of the process solution. We assume that we observe a sample (XkΔ)0≤k≤n+1. Our aim is to study nonparametric estimators of the drift function b(.), under general conditions. We propose projection estimators based on a least-squares type contrast and, in order to generalize existing results, we want to consider possibly non compactly supported projection bases and possibly non bounded volatility. To that aim, we relate the model with a simpler regression model, then to a more elaborate heteroscedastic model, plus some residual terms. This allows to see the role of heteroscedasticity first and the role of dependency between the variables and to present different probabilistic tools used to face each part of the problem. For each step, we try to see the “price” of each assumption. This is the developed version of the talk given in August 2018 in Dijon, Journées MAS.

scholarly journals Estimating Regression Function of Multi-response Semiparametric Regression Model Using Smoothing Spline

2020 ◽  
Vol 55 (5) ◽  
Author(s):  
Budi Lestari ◽  
Nur Chamidah
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Goodness Of Fit ◽  
Weighted Least Squares ◽  
Estimation Method ◽  
Semiparametric Regression ◽  
Regression Function ◽  
Smoothing Spline ◽  
Semiparametric Regression Model ◽  
Research Results

The article describes a new estimation method of regression functions in a multi-response semiparametric regression model based on smoothing spline. The multi-response semiparametric regression model is a combined model between a parametric regression model and a nonparametric regression model, where there is a correlation between responses. The proposed estimation method enhances the flexibility of the multi-response semiparametric regression model by combining a goodness of fit function and a penalty function to calculate an estimation which not only considers the goodness of fitting of the model, but also the smoothness of the estimation model curve. The optimal trade-off between goodness and smoothness can be achieved by selecting the optimal smoothing parameters. The article discusses a theoretically proposed method for estimating this multi-response semiparametric regression model regression function of parametric and nonparametric components. We use the weighted least squares method to estimate the parametric component parameters, we determine the goodness of fit and penalty functions using the reproducing kernel Hilbert space method, and then take the result of penalized weighted least squares optimization to obtain an estimate of the nonparametric component. The new research results are a weighted least squares estimator of parameters of parametric components, and a weighted partial smoothing spline estimator of the nonparametric component. The result shows that the estimated multi-response semiparametric regression model is linear to the observation, and is a combination of the estimations of the parametric and nonparametric components. The research results of the estimation of this model can be applied to medical fields for predictive purposes.

scholarly journals Asymptotic normality of the least squares estimate in trigonometric regression with strongly dependent noise

2019 ◽  
pp. 24-41
Author(s):  
T. O. Drabyk ◽  
O. V. Ivanov
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Least Squares ◽  
Regression Function ◽  
Least Squares Estimator ◽  
Stationary Time Series ◽  
Model Parameters ◽  
Local Transformation ◽  
Stationary Time ◽  
Trigonometric Regression

The least squares estimator asymptotic properties of the parameters of trigonometric regression model with strongly dependent noise are studied. The goal of the work lies in obtaining the requirements to regression function and time series that simulates the random noise under which the least squares estimator of regression model parameters are asymptotically normal. Trigonometric regression model with discrete observation time and open convex parametric set is research object. Asymptotic normality of trigonometric regression model parameters the least squares estimator is research subject. For obtaining the thesis results complicated concepts of time series theory and time series statistics have been used, namely: local transformation of Gaussian stationary time series, stationary time series with singular spectral density, spectral measure of regression function, admissibility of singular spectral density of stationary time series in relation to this measure, expansions by Chebyshev-Hermite polynomials of the transformed Gaussian time series values and it’s covariances, central limit theorem for weighted vector sums of the values of such a local transformation and Brouwer fixed point theorem.

scholarly journals The Role of Managerial Commitment and TPM Implementation Strategies in Productivity Benefits

2018 ◽  
Vol 8 (7) ◽  
pp. 1153 ◽  
Author(s):  
José Díaz-Reza ◽  
Jorge García-Alcaraz ◽  
Liliana Avelar-Sosa ◽  
José Mendoza-Fong ◽  
Juan Sáenz Diez-Muro ◽  
...  
Keyword(s):  
Least Squares ◽  
Structural Equation Model ◽  
Preventive Maintenance ◽  
Latent Variables ◽  
Structural Equation ◽  
Manufacturing Industry ◽  
Implementation Strategies ◽  
Equation Model ◽  
Total Productive Maintenance

The present research proposes a structural equation model to integrate four latent variables: managerial commitment, preventive maintenance, total productive maintenance, and productivity benefits. In addition, these variables are related through six research hypotheses that are validated using collected data from 368 surveys administered in the Mexican manufacturing industry. Consequently, the model is evaluated using partial least squares. The results show that managerial commitment is critical to achieve productivity benefits, while preventive maintenance is indispensable to total preventive maintenance. These results may encourage company managers to focus on managerial commitment and implement preventive maintenance programs to guarantee the success of total productive maintenance.

scholarly journals Initial coin offerings and the cryptocurrency hype - the moderating role of exogenous and endogenous signals

2021 ◽  
Author(s):  
Ferdinand Thies ◽  
Sören Wallbach ◽  
Michael Wessel ◽  
Markus Besler ◽  
Alexander Benlian
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
High Quality ◽  
Academic Interest ◽  
Entrepreneurial Ventures ◽  
Asset Class ◽  
The Impact ◽  
Moderating Role ◽  
Initial Coin Offerings

AbstractInitial coin offerings (ICOs) have recently emerged as a new financing instrument for entrepreneurial ventures, spurring economic and academic interest. Nevertheless, the impact of exogenous and endogenous signals on the performance of ICOs as well as the effects of the cryptocurrency hype and subsequent downfall of Bitcoin between 2016 and 2019 remain underexplored. We applied ordinary least squares (OLS) regressions based on a dataset containing 1597 ICOs that covers almost 2.5 years. The results show that exogenous and endogenous signals have a significant effect on the funds raised in ICOs. We also find that the Bitcoin price heavily drives the performance of ICOs. However, this hype effect is moderated, as high-quality ICOs are not pegged to these price developments. Revealing the interplay between hypes and signals in the ICO’s asset class should broaden the discussion of this emerging digital phenomenon.

Brothers or others in arms? Civilian constituencies and rebel fragmentation in civil war

2017 ◽  
Vol 55 (1) ◽  
pp. 62-77 ◽  
Author(s):  
Eric S Mosinger
Keyword(s):  
Social Networks ◽  
Regression Analysis ◽  
Collective Action ◽  
Diffusion Model ◽  
Theoretical Framework ◽  
Quantitative Evidence ◽  
Rebel Groups ◽  
Two Factors ◽  
Cross National

Why do united rebel fronts emerge in some insurgencies, while in other insurgencies multiple rebel groups mobilize independently to challenge the state, and often, each other? I develop a diffusion model of rebel fragmentation in which participation in rebellion spreads, completely or incompletely, through networks of civilians and dissidents. Using this theoretical framework I hypothesize that two factors jointly determine whether a rebel movement remains unified or fragments: the rebels’ investment in civilian mobilization, and the overall level of civilian grievances. The theory predicts that widely shared grievances motivate the formation of many small dissident groups willing to challenge the regime. Given the difficulty of collective action between disparate opposition actors, an emerging rebel movement will tend towards fragmentation when popular grievances are high. Yet extremely high civilian grievances can also help rebels activate broad, overlapping civilian social networks that serve to bridge together dissident groups. Mass-mobilizing rebel groups, benefiting from the participation of broad civilian networks, are most likely to forge and maintain a unified rebel front. I test this theory alongside several alternatives drawn from cross-national studies of conflict using regression analysis. The quantitative evidence lends considerable credence to the role of rebel constituencies in preventing or fomenting rebel fragmentation.

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