scholarly journals Cross-Validation Model Averaging for Generalized Functional Linear Model

Econometrics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 7
Author(s):  
Haili Zhang ◽  
Guohua Zou
Keyword(s):  
Linear Model ◽  
Functional Data ◽  
Cross Validation ◽  
House Price ◽  
Model Averaging ◽  
Estimation Accuracy ◽  
Functional Linear Model ◽  
Model Set ◽  
Function Predictor ◽  
Better Than

Functional data is a common and important type in econometrics and has been easier and easier to collect in the big data era. To improve estimation accuracy and reduce forecast risks with functional data, in this paper, we propose a novel cross-validation model averaging method for generalized functional linear model where the scalar response variable is related to a random function predictor by a link function. We establish asymptotic theoretical result on the optimality of the weights selected by our method when the true model is not in the candidate model set. Our simulations show that the proposed method often performs better than the commonly used model selection and averaging methods. We also apply the proposed method to Beijing second-hand house price data.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

scholarly journals Bayesian Estimation of the Functional Spatial Lag Model

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Alassane Aw ◽  
Emmanuel Nicolas Cabral
Keyword(s):  
Bayesian Estimation ◽  
Linear Model ◽  
Unemployment Rate ◽  
Simulation Study ◽  
Data Modeling ◽  
Functional Linear Model ◽  
Bayesian Mcmc ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Lag Model

AbstractThe spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM) model. The Bayesian MCMC technique is used as a method of estimation for the parameters of the model. A simulation study is conducted in order to compare the results of the Bayesian functional spatial lag model with the functional spatial lag model and the functional linear model. As an illustration, the proposed Bayesian functional spatial lag model is used to establish a relationship between the unemployment rate and the curves of illiteracy rate observed in the 45 departments of Senegal.

scholarly journals Three State Estimation Fusion Methods Based on the Characteristic Function Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1440
Author(s):  
Yiran Yuan ◽  
Chenglin Wen ◽  
Yiting Qiu ◽  
Xiaohui Sun
Keyword(s):  
Characteristic Function ◽  
State Estimation ◽  
Packet Loss ◽  
Estimation Accuracy ◽  
Measurement Systems ◽  
Fusion Methods ◽  
Estimation Fusion ◽  
Parallel Filter ◽  
Sequential Filter ◽  
Better Than

There are three state estimation fusion methods for a class of strong nonlinear measurement systems, based on the characteristic function filter, namely the centralized filter, parallel filter, and sequential filter. Under ideal communication conditions, the centralized filter can obtain the best state estimation accuracy, and the parallel filter can simplify centralized calculation complexity and improve feasibility; in addition, the performance of the sequential filter is very close to that of the centralized filter and far better than that of the parallel filter. However, the sequential filter can tolerate non-ideal conditions, such as delay and packet loss, and the first two filters cannot operate normally online for delay and will be invalid for packet loss. The performance of the three designed fusion filters is illustrated by three typical cases, which are all better than that of the most popular Extended Kalman Filter (EKF) performance.

On Performance of Combining Methods for Three-Node Relay Network

2012 ◽  
Vol 263-266 ◽  
pp. 1160-1164
Author(s):  
Wen Yuan Rao
Keyword(s):  
Relay Network ◽  
Estimation Accuracy ◽  
Diversity Order ◽  
Amplify And Forward ◽  
Decode And Forward ◽  
Channel Quality ◽  
Combining Methods ◽  
Better Than

We study the performance of the three-node relay network. Three combining methods for the Amplify-and-Forward (AF) protocol and the Decode-and-Forward (DF) protocol are compared. Simulations indicate that the AF protocol is better than DF under all these three combining methods. To combine the incoming signals the channel quality should be estimated as accuracy as possible, more estimation accuracy requires more resource. A very simple combining method can obtain the performance comparative with optimal combining methods approximately. At the same time, all three combining methods for both diversity protocols can achieve the maximum diversity order.

To the problem of uncertainty in interpolation of annual runoff

2012 ◽  
Vol 43 (6) ◽  
pp. 833-850 ◽  
Author(s):  
Ziqi Yan ◽  
Lars Gottschalk ◽  
Irina Krasovskaia ◽  
Jun Xia
Keyword(s):  
Cross Validation ◽  
Drainage Basin ◽  
Annual Runoff ◽  
Mean Value ◽  
Regular Grid ◽  
Main River ◽  
Average Annual Runoff ◽  
The One ◽  
Better Than

The long-term mean value of runoff is the basic descriptor of available water resources. This paper focuses on the accuracy that can be achieved when mapping this variable across space and along main rivers for a given stream gauging network. Three stochastic interpolation schemes for estimating average annual runoff across space are evaluated and compared. Two of the schemes firstly interpolate runoff to a regular grid net and then integrate the grid values along rivers. One of these schemes includes a constraint to account for the lateral water balance along the rivers. The third scheme interpolates runoff directly to points along rivers. A drainage basin in China with 20 gauging sites is used as a test area. In general, all three approaches reproduce the sample discharges along rivers with postdiction errors along main river branches around 10%. Using more objective cross-validation results, it was found that the two schemes based on basin integration, and especially the one with a constraint, performed significantly better than the one with direct interpolation to points along rivers. The analysis did not allow identification of possible influence of surface water use.

scholarly journals Intelligent Prediction of Sieving Efficiency in Vibrating Screens

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Bin Zhang ◽  
Jinke Gong ◽  
Wenhua Yuan ◽  
Jun Fu ◽  
Yi Huang
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Prediction Performance ◽  
Least Square ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Screen Length ◽  
Vibrating Screen ◽  
Working Parameters ◽  
Better Than

In order to effectively predict the sieving efficiency of a vibrating screen, experiments to investigate the sieving efficiency were carried out. Relation between sieving efficiency and other working parameters in a vibrating screen such as mesh aperture size, screen length, inclination angle, vibration amplitude, and vibration frequency was analyzed. Based on the experiments, least square support vector machine (LS-SVM) was established to predict the sieving efficiency, and adaptive genetic algorithm and cross-validation algorithm were used to optimize the parameters in LS-SVM. By the examination of testing points, the prediction performance of least square support vector machine is better than that of the existing formula and neural network, and its average relative error is only 4.2%.

scholarly journals Meta-Learning PAC-Bayes Priors in Model Averaging

2020 ◽  
Vol 34 (04) ◽  
pp. 4198-4205
Author(s):  
Yimin Huang ◽  
Weiran Huang ◽  
Liang Li ◽  
Zhenguo Li
Keyword(s):  
Model Averaging ◽  
Selection Procedure ◽  
Real Data ◽  
Poor Quality ◽  
Quality Data ◽  
Main Challenge ◽  
Meta Learning ◽  
Model Set ◽  
Base Learner ◽  
Proper Priors

Nowadays model uncertainty has become one of the most important problems in both academia and industry. In this paper, we mainly consider the scenario in which we have a common model set used for model averaging instead of selecting a single final model via a model selection procedure to account for this model's uncertainty in order to improve reliability and accuracy of inferences. Here one main challenge is to learn the prior over the model set. To tackle this problem, we propose two data-based algorithms to get proper priors for model averaging. One is for meta-learner, the analysts should use historical similar tasks to extract the information about the prior. The other one is for base-learner, a subsampling method is used to deal with the data step by step. Theoretically, an upper bound of risk for our algorithm is presented to guarantee the performance of the worst situation. In practice, both methods perform well in simulations and real data studies, especially with poor quality data.

scholarly journals PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yanjuan Li ◽  
Zitong Zhang ◽  
Zhixia Teng ◽  
Xiaoyan Liu
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Prediction Model ◽  
Multilayer Perceptron ◽  
Type Ii Diabetes ◽  
Cross Validation ◽  
Experimental Results ◽  
Type Ii ◽  
Fold Cross Validation ◽  
Better Than

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer’s disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.

scholarly journals Measures of influence for the functional linear model with scalar response

2010 ◽  
Vol 101 (2) ◽  
pp. 327-339 ◽  
Author(s):  
Manuel Febrero-Bande ◽  
Pedro Galeano ◽  
Wenceslao González-Manteiga
Keyword(s):  
Linear Model ◽  
Functional Linear Model

scholarly journals On-the-Fly Prediction of Orbit Corrections for RTK Positioning

2006 ◽  
Vol 59 (2) ◽  
pp. 321-334 ◽  
Author(s):  
Ahmed El-Mowafy
Keyword(s):  
Equations Of Motion ◽  
Estimation Accuracy ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Orbital Correction ◽  
Exponential Smoothing Method ◽  
The Impact ◽  
Multiple Reference ◽  
Selection Of ◽  
Better Than

In this study, a method is presented to maintain real-time positioning at the decimetre-level accuracy during breaks in reception of the measurement corrections from multiple reference stations. The method is implemented at the rover by estimating prediction coefficients of the corrections during normal RTK positioning, and uses these coefficients to predict the corrections when reception of the corrections is temporarily lost. The paper focuses on one segment of this method, the on-the-fly prediction of orbital corrections. Frequently, only a few minutes of data representing short orbit ‘arcs’ are available to the user before losing radio transmission. Thus, it would be hard for the rover to predict the satellite positions using equations of motion. An alternative method is proposed. In this method, GPS orbital corrections are predicted as a time series and are added to the initial positions computed from the broadcast ephemeris to compute relatively accurate satellite positions. Different prediction approaches were investigated. Results show that the double exponential smoothing method and Winters' method can be successfully applied. The latter, however, has a better performance. The impact of the data length used for estimation of the prediction coefficients and the selection of seasonal lengths in Winters' method were investigated and some values were recommended. In general, the method can give orbital correction estimation accuracy of less than 5 cm after 15 minutes of prediction. This will result in a positioning accuracy better than 5 cm.

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