aggregation process in parameter estimation

Parameter-Based Data Aggregation for Statistical ...

the sensory data, it will suffice if aggregation algorithms return the probability distribution of the sensory data. In this section, we present the theoretical foundation, describe the process of aggregation, and formulate and solve the problem of distribu-tion parameter estimation by leveraging general mixture model techniques.

Compression and Aggregation of Bayesian Estimates for Data ...

port high-quality aggregation of Bayesian estimation for statistical models. In the proposed approach, we compress each data segment by retaining only the model parameters and some auxiliary measures. We then develop an aggregation formula that allows us to reconstruct the Bayesian estimation

(PDF) On Decomposition and Aggregation Error in Estimation ...

(14) have suggested that "the parametric sensitivity of a more detailed model and its potential to propagate errors may mask the underlying contrast in the data and create problems for parameter estimation."By contrast, the reverse is likely to be true in multiplicative models (e.g., for estimating the frequency of a sequence of events).

Bayesian aggregation of two forecasts in the partial ...

in a setting where parameter estimation is not required. We proceed to provide an explicit formula for a “one-shot” aggregation problem with two forecasters. Keywords: Expert, probability forecast, Gaussian process, judgmental forecasting 2010 MSC: Primary: 62C10, Secondary: 60G15 1.Introduction

Aggregation Among Binary, Count, and Duration Models ...

To deal with aggregation bias appropriately in these models, two steps are necessary. First should come models, such as those provided in this paper, which at least under certain specific assumptions are able to estimate the same parameters no matter what level of analysis or type of aggregation

THE EFFECT OF SMOOTHING PARAMETER IN KERNELS

Smoothing Parameter Selection in Kernel Aggregation Appropriate selection of the smoothing parameter is often critical to the process of kernel aggregation in kernel density estimation because its performance is based on its right selection. The quality of the estimates in Equation (4) and Equation (6) is measured by the

Parameter-Based Data Aggregation for Statistical ...

the sensory data, it will suffice if aggregation algorithms return the probability distribution of the sensory data. In this section, we present the theoretical foundation, describe the process of aggregation, and formulate and solve the problem of distribu-tion parameter estimation by leveraging general mixture model techniques.

Compression and Aggregation of Bayesian Estimates for

port high-quality aggregation of Bayesian estimation for statistical models. In the proposed approach, we compress each data segment by retaining only the model parameters and some auxiliary measures. We then develop an aggregation formula that allows us to reconstruct the Bayesian estimation from partitioned

Aggregation Among Binary, Count, and Duration Models ...

To deal with aggregation bias appropriately in these models, two steps are necessary. First should come models, such as those provided in this paper, which at least under certain specific assumptions are able to estimate the same parameters no matter what level of analysis or type of aggregation produced the available data.

Estimation of aggregation kernels based on Laurent ...

Aug 04, 2017  The dynamics of the aggregation process described by Eq. are governed by the aggregation kernel k, which is assFor developing and assessing the estimation procedure described below, we use three different kernel functions which are given in Table 1 and shown in Fig. 1.These kernels are chosen to represent qualitatively different curvatures and

Phase-Wise Parameter Aggregation for Improving SGD ...

ours that operate on the optimization process itself during training. In [27], the aggregation weights are adaptively learned, though requiring k-times extra-memory to store k multiple model parameters. Toward faster convergence, the Lookahead method [36] efficiently applies model aggrega-tion every k updates by means of moving average which

THE EFFECT OF SMOOTHING PARAMETER IN KERNELS

Smoothing Parameter Selection in Kernel Aggregation Appropriate selection of the smoothing parameter is often critical to the process of kernel aggregation in kernel density estimation because its performance is based on its right selection. The quality of the estimates in Equation (4) and Equation (6) is measured by the

Quantitative dynamics of reversible platelet aggregation ...

Parameter values of the model were determined by means of parameter estimation techniques implemented in COPASI software. The mathematical model was able to describe reversible platelet aggregation LTA curves without assuming changes in platelet aggregation parameters over time, but with the assumption that platelet can enter the aggregate only ...

15.450 Lecture 7, Parameter estimation

the distribution, e.g., the first two moments, we can still estimate the parameters by the quasi-MLE method. Alternatively, if we only know a few moments of the distribution, but not the entire pdf p(X ,θ 0), we can estimate parameters by the Generalized Method of Moments (GMM). QMLE and GMM methods are less precise (efficient) than MLE, but

Spatial aggregation and soil process modelling - ScienceDirect

Apr 01, 1999  Nonlinear soil process models that are defined and calibrated at the point support cannot at the same time be valid at the block support. This means that in the situation where model input is available at point support and where model output is required at block support, spatial aggregation should take place after the model is run.

Gaussian Process Hyperparameter Estimation – Quantitative ...

May 16, 2016  The Gaussian Process. The GP is a Bayesian method and as such, there is a prior, there is data, and there is a posterior that is the prior conditioned on the data. ... These three plots show the posterior for the 5% to 95% parameter estimation for both ...

Approach to theoretical estimation of the activation ...

Oct 20, 2015  Even though the estimation of α is rather rough, the experimental results shown in the following sections will verify that α ≈ 0.2 is an appropriate value. Therefore, in the collision process of aggregation, the kinetic energy of a Brownian particle (∼0.2 kT) is much less than the instantaneous kinetic energy (0.5 kT).

Statistical Model Aggregation via Parameter Matching

infinitely many. The generative process is formally characterized through a Beta-Bernoulli process (BBP) [31]. Model fusion, rather than being an ad-hoc procedure, then reduces to posterior inference over the meta-model. Governed by the BBP posterior, the meta-model allows local parameters to either match existing global parameters or create ...

GHG Protocol guidance on uncertainty assessment in GHG ...

inventory is the uncertainties associated with parameters (e.g. activity data, emission factors, and 3 The role of expert judgment in the assessment of the parameter can be twofold: Firstly, expert judgment can be the source of the data that are necessary to estimate the parameter. Secondly, expert judgment can help (in combination with

Labor-Market Heterogeneity, Aggregation, and Policy (IN ...

Jan 01, 2013  Fernández-Villaverde and Rubio-Ramiréz (2008) estimate a model in which both monetary policy rule parameters and nominal rigidity parameters are allowed to vary over time. They find that during high-inflation episodes, the estimated cost associated with nominal price changes is lower and they interpret the negative correlation between policy ...

(PDF) Parameter-Based Data Aggregation for Statistical ...

We also design, in this paper, an accurate estimation of the distribution parameters of sensory data using the expectation-maximization (EM) algorithm. ... The general process of our aggregation ...

Compression and Aggregation of Bayesian Estimates for

port high-quality aggregation of Bayesian estimation for statistical models. In the proposed approach, we compress each data segment by retaining only the model parameters and some auxiliary measures. We then develop an aggregation formula that allows us to reconstruct the Bayesian estimation

Statistical Model Aggregation via Parameter Matching

infinitely many. The generative process is formally characterized through a Beta-Bernoulli process (BBP) [31]. Model fusion, rather than being an ad-hoc procedure, then reduces to posterior inference over the meta-model. Governed by the BBP posterior, the meta-model allows local parameters to either match existing global parameters or create ...

Estimation of aggregation kernels based on Laurent ...

Aug 04, 2017  The dynamics of the aggregation process described by Eq. are governed by the aggregation kernel k, which is assFor developing and assessing the estimation procedure described below, we use three different kernel functions which are given in Table 1 and shown in Fig. 1.These kernels are chosen to represent qualitatively different curvatures and dependencies on the particle

Phase-Wise Parameter Aggregation for Improving SGD ...

ours that operate on the optimization process itself during training. In [27], the aggregation weights are adaptively learned, though requiring k-times extra-memory to store k multiple model parameters. Toward faster convergence, the Lookahead method [36] efficiently applies model aggrega-tion every k updates by means of moving average which

A flexible method for aggregation of prior statistical ...

Apr 06, 2017  Second, in the process of estimating the meta-model we included parameters that represent the biases in different methods for the measurement of BMR and fat mass. Therefore, this application of GMA also provides estimates for how different measurement methods compare with each other, which can be used to calibrate different measurement methods ...

THE EFFECT OF SMOOTHING PARAMETER IN KERNELS

Smoothing Parameter Selection in Kernel Aggregation Appropriate selection of the smoothing parameter is often critical to the process of kernel aggregation in kernel density estimation because its performance is based on its right selection. The quality of the estimates in Equation (4) and Equation (6) is measured by the

15.450 Lecture 7, Parameter estimation

the distribution, e.g., the first two moments, we can still estimate the parameters by the quasi-MLE method. Alternatively, if we only know a few moments of the distribution, but not the entire pdf p(X ,θ 0), we can estimate parameters by the Generalized Method of Moments (GMM). QMLE and GMM methods are less precise (efficient) than MLE, but

Chapter 4 Parameter Estimation

Nov 06, 2012  English parameter q differs from π), because it ignores the data completely. Consistency is nearly always a desirable property for a statistical estimator. 4.2.2 Bias If we view the collection (or sampling) of data from which to estimate a population pa-rameter as a stochastic process, then the parameter estimate θˆ η resulting from applying a

Gaussian Process Hyperparameter Estimation – Quantitative ...

May 16, 2016  The Gaussian Process. The GP is a Bayesian method and as such, there is a prior, there is data, and there is a posterior that is the prior conditioned on the data. ... These three plots show the posterior for the 5% to 95% parameter estimation for both ...

4 Tools to Estimate Costs in the Project Management PM ...

Jun 04, 2012  Cost estimation is the process of forecasting the project’s cost with a defined scope. ... you can calculate the cost of other parameters: human resources, materials, equipment, etc. ... this is the aggregation of all “activity level” estimations to come up with the Project level estimation. I read in one article that the activity can be ...

Labor-Market Heterogeneity, Aggregation, and Policy (IN ...

Jan 01, 2013  Fernández-Villaverde and Rubio-Ramiréz (2008) estimate a model in which both monetary policy rule parameters and nominal rigidity parameters are allowed to vary over time. They find that during high-inflation episodes, the estimated cost associated with nominal price changes is lower and they interpret the negative correlation between policy ...

Aggregation Process for Software Engineering

the treatments are significant. In contrast, the idea behind running an aggregation process is to get an improvement index, indicating how much better one treatment is than the other. Therefore, aggregation methods should be classed as parameter estimation methods rather than hypothesis testing methods, even though their results

On Sequential Parameter Estimation of a Linear Regression ...

Then, in particular, the observation process (X (t)) is well de ned for allt > 0: The problem is to estimate the unknown vector# with given accuracy in the sense (3) from the observation of (X;a) = ( X (t);a(t)) t 0: In Galtchouk and Konev (2001) has been constructed the general sequential parameter estimation