nonparametric model
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2022 ◽  
Vol 10 (4) ◽  
pp. 605-616
Author(s):  
Jody Hendrian ◽  
Suparti Suparti ◽  
Alan Prahutama

Investing in gold is a flexible choice because it can be sold at any time and used as an emergency fund. Investors should have the knowledge to predict data from time to time to achieve investment goals. One of the statistical methods for time series data modeling is ARIMA. The ARIMA model is strict with the assumptions that the data must be stationary, the residuals must be normally distributed, independent, and with constant variance, so an alternative model is proposed, namely nonparametric regression model, which has no modeling assumptions requirement. In this study, the daily world gold price data will be modeled using a local polynomial nonparametric model as an alternative because the assumptions in the ARIMA are not fulfilled. The data is divided into 2 parts, namely in sample data from January 2, 2020 to November 30, 2020 to form a model and out sample data from December 1, 2020 to December 31, 2020 used for evauation of model performance based on MAPE values. The chosen best model is the local polynomial model with Gaussian kernel function of degree 5, bandwidth of 373, and local point of 1744 with an MSE value of 482.6420. The local polynomial model out sample data MAPE value is 0.61%, indicating that the model has excellent forecasting capability. In this study, Graphical User Interface (GUI) using R software with the help of shiny package is also built, making data analyzing easier and generating more interactive display output. 


2022 ◽  
Author(s):  
Joonho Bae ◽  
Jinkyoo Park ◽  
Jeonghye Choi ◽  
Seung Bum Soh

Pharmaceutics ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2170
Author(s):  
Femke de Velde ◽  
Brenda C. M. de Winter ◽  
Michael N. Neely ◽  
Jan Strojil ◽  
Walter M. Yamada ◽  
...  

Population pharmacokinetic modeling and simulation (M&S) are used to improve antibiotic dosing. Little is known about the differences in parametric and nonparametric M&S. Our objectives were to compare (1) the external validation of parametric and nonparametric models of imipenem in critically ill patients and (2) the probability of target attainment (PTA) calculations using simulations of both models. The M&S software used was NONMEM 7.2 (parametric) and Pmetrics 1.5.2 (nonparametric). The external predictive performance of both models was adequate for eGFRs ≥ 78 mL/min but insufficient for lower eGFRs, indicating that the models (developed using a population with eGFR ≥ 60 mL/min) could not be extrapolated to lower eGFRs. Simulations were performed for three dosing regimens and three eGFRs (90, 120, 150 mL/min). Fifty percent of the PTA results were similar for both models, while for the other 50% the nonparametric model resulted in lower MICs. This was explained by a higher estimated between-subject variability of the nonparametric model. Simulations indicated that 1000 mg q6h is suitable to reach MICs of 2 mg/L for eGFRs of 90–120 mL/min. For MICs of 4 mg/L and for higher eGFRs, dosing recommendations are missing due to largely different PTA values per model. The consequences of the different modeling approaches in clinical practice should be further investigated.


Author(s):  
Nils Lid Hjort ◽  
Emil Aas Stoltenberg

AbstractAalen’s linear hazard rate regression model is a useful and increasingly popular alternative to Cox’ multiplicative hazard rate model. It postulates that an individual has hazard rate function $$h(s)=z_1\alpha _1(s)+\cdots +z_r\alpha _r(s)$$ h ( s ) = z 1 α 1 ( s ) + ⋯ + z r α r ( s ) in terms of his covariate values $$z_1,\ldots ,z_r$$ z 1 , … , z r . These are typically levels of various hazard factors, and may also be time-dependent. The hazard factor functions $$\alpha _j(s)$$ α j ( s ) are the parameters of the model and are estimated from data. This is traditionally accomplished in a fully nonparametric way. This paper develops methodology for estimating the hazard factor functions when some of them are modelled parametrically while the others are left unspecified. Large-sample results are reached inside this partly parametric, partly nonparametric framework, which also enables us to assess the goodness of fit of the model’s parametric components. In addition, these results are used to pinpoint how much precision is gained, using the parametric-nonparametric model, over the standard nonparametric method. A real-data application is included, along with a brief simulation study.


Author(s):  
Amir Ali Nasrollahzadeh ◽  
Amin Khademi

Identifying the right dose is one of the most important decisions in drug development. Adaptive designs are promoted to conduct dose-finding clinical trials as they are more efficient and ethical compared with static designs. However, current techniques in response-adaptive designs for dose allocation are complex and need significant computational effort, which is a major impediment for implementation in practice. This study proposes a Bayesian nonparametric framework for estimating the dose-response curve, which uses a piecewise linear approximation to the curve by consecutively connecting the expected mean response at each dose. Our extensive numerical results reveal that a first-order Bayesian nonparametric model with a known correlation structure in prior for the expected mean response performs competitively when compared with the standard approach and other more complex models in terms of several relevant metrics and enjoys computational efficiency. Furthermore, structural properties for the optimal learning problem, which seeks to minimize the variance of the target dose, are established under this simple model. Summary of Contribution: In this work, we propose a methodology to derive efficient patient allocation rules in response-adaptive dose-finding clinical trials, where computational issues are the main concern. We show that our methodologies are competitive with the state-of-the-art methodology in terms of solution quality, are significantly more computationally efficient, and are more robust in terms of the shape of the dose-response curve, among other parameter changes. This research fits in “the intersection of computing and operations research” as it adapts operations research techniques to produce computationally attractive solutions to patient allocation problems in dose-finding clinical trials.


Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Gang Chen ◽  
Wei Wang ◽  
Yifan Xue

The system identification of a ship dynamics model is crucial for the intelligent navigation and design of the ship’s controller. The fluid dynamic effect and the complicated geometry of the hull surface cause a nonlinear or asymmetrical behavior, and it is extremely difficult to establish a ship dynamics model. A nonparametric model based on sparse Gaussian process regression with similarity was proposed for the dynamic modeling of a ship. It solves the problem, wherein the kernel method is difficult to apply to big data, using similarity to sparse large sample datasets. In addition, the experimental data of the KVLCC2 ship are used to verify the validity of the proposed method. The results show that sparse Gaussian process regression with similarity can be applied to the learning of a large sample data, in order to obtain ship motion prediction with higher accuracy than the parameterized model. Moreover, in the case of sensor signal loss, the identified model continues to provide accurate ship speed and trajectory information in the future, and the maximum prediction error of the motion trajectory within 100 s is only 0.59 m.


2021 ◽  
Author(s):  
Sunny Cui ◽  
Elizabeth Yoo ◽  
Didong Li ◽  
Krzysztof Laudanski ◽  
Barbara Engelhardt

Gaussian processes (GPs) are a versatile nonparametric model for nonlinear regression and have been widely used to study spatiotemporal phenomena. However, standard GPs offer limited interpretability and generalizability for datasets with naturally occurring hierarchies. With large-scale, rapidly-updating electronic health record (EHR) data, we want to study patient trajectories across diverse patient cohorts while preserving patient subgroup structure. In this work, we partition our cohort of over 2000 COVID-19 patients by sex and ethnicity. We develop and apply a hierarchical Gaussian process and a mixture of experts (MOE) hierarchical GP model to fit patient trajectories on clinical markers of disease progression. A case study for albumin, an effective predictor of COVID-19 patient outcomes, highlights the predictive performance of these models. These hierarchical spatiotemporal models of EHR data bring us a step closer toward our goal of building flexible approaches to capture patient data that can be used in real-time systems.


2021 ◽  
Vol 13 (16) ◽  
pp. 9462
Author(s):  
Yadira Pazmiño ◽  
José Juan de Felipe ◽  
Marc Vallbé ◽  
Franklin Cargua ◽  
Luis Quevedo

Páramo ecosystems harbor important biodiversity and provide essential environmental services such as water regulation and carbon sequestration. Unfortunately, the scarcity of information on their land uses makes it difficult to generate sustainable strategies for their conservation. The purpose of this study is to develop a methodology to easily monitor and document the conservation status, degradation rates, and land use changes in the páramo. We analyzed the performance of two nonparametric models (the CART decision tree, CDT, and multivariate adaptive regression curves, MARS) in the páramos of the Chambo sub-basin (Ecuador). We used three types of attributes: digital elevation model (DEM), land use cover (Sentinel 2), and organic carbon content (Global Soil Organic Carbon Map data, GSOC) and a categorical variable, land use. We obtained a set of selected variables which perform well with both models, and which let us monitor the land uses of the páramos. Comparing our results with the last report of the Ecuadorian Ministry of Environment (2012), we found that 9% of the páramo has been lost in the last 8 years.


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