Parameter-Based Data Aggregation for Statistical ... 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.

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

Using these models, the possible benefits of data aggregation with regards to parameter estimation are investigated by means of a simulation study. The application made with reference to the ARMA(1,1) model shows advantageous effects of data aggregation, while the same benefits are not found for estimation of the conceptual parameters with the ...

(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).

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

Nov 01, 1998 The parameter estimation shown in Fig. 9, however, indicates a collision efficiency of only 2×10 −4, despite the fact that this was close to the most rapid aggregation observable under quiescent conditions. Table 2 shows values of collision efficiency estimated by other researchers under various conditions.

Jun 01, 2020 In this paper, a cell average technique (CAT) based parameter estimation method is proposed for cooling crystallization involved with particle growth, aggregation and breakage, by establishing a more efficient and accurate solution in terms of the automatic differentiation (AD) algorithm.

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

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

time aggregation while the elasticity of output with respect to average hours of work increases.Section 4 considers the time aggregation effect explicitly and reports Monte-Carlo simulations showing thatthere is a bias in the aggregation process that explains the results obtained here and in the literature.The estimation bias of theoutput-

how parameters of a distribution of the random coeﬃcients can be estimated and examples for possible distributions are given. Keywords: random coeﬃcient AR(2), least square, aggregation, parameter estimation, central limit theorem 1

(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).

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

Using these models, the possible benefits of data aggregation with regards to parameter estimation are investigated by means of a simulation study. The application made with reference to the ARMA(1,1) model shows advantageous effects of data aggregation, while the same benefits are not found for estimation of the conceptual parameters with the ...

Batch tests were employed to estimate the optimal conditions for improving the settleability of activated sludge through aggregation under magnetic field. A four – factor central composite design (CCD) was employed to find out the interaction effects of the variables while response surface methodology (RSM) was utilized for process optimization.

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

1 to 7) and showed that conceptual parameters of models of monthly and T-day runoff are more efficiently estimated using different scales of aggregation. An attempt to introduce a more systematic procedure in the selection of the optimal time scale for the estimation of each parameter is made in this paper. In this direction,

Parameter estimation of Gaussian stationary processes 407 X t,wecannotethatthevariableϕ(t)iscenteredgaussian,hencetheexpected value of ϕ(t)iszeroanditsvarianceisE[ϕ(t)2]=V(θ 0),asfollows: E[ϕ(t)2]= L q,q =0 a qa q E[X t−αqX t−αq] L q,q =0 a qa q ρ θ 0 (αq−q )L k=0 b kρ θ 0 (αk)=V(θ0). Following the notation of [36] we deﬁne the vector of

Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data.

We propose a new alternative method to estimate the parameters in one-factor mean reversion processes based on the maximum likelihood technique. This approach makes use of Euler-Maruyama scheme to approximate the continuous-time model and build a new process discretized. The closed formulas for the estimators are obtained. Using simulated data series, we compare the results

has a signature raw state, deﬁned either through the aggregation process or the disaggregation process. Deﬁnition 1 (Anchor State). A state iis called an “aggregation anchor state” of the meta-state k if Uik = 1 and Uis = 0 for all s6= k. A state jis called a “disaggregation anchor state” of the

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).

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.

The basic statistical problem of aggregation theory is, given a sample {Y 1(N), , Y n (N)} of size n of the N-fold aggregated process, to draw conclusions for the structure of the constituting ...

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

Statistical approach to aggregation of production functions 6675 Subsequently, parameters of the obtained macro production function were estimated. Obviously, one can suggest the following methods for estimating parameters of the obtained function: – substitution of estimated mean and estimated variance of TFP parameters;

Batch tests were employed to estimate the optimal conditions for improving the settleability of activated sludge through aggregation under magnetic field. A four – factor central composite design (CCD) was employed to find out the interaction effects of the variables while response surface methodology (RSM) was utilized for process optimization.

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).

The modelled aggregation process depends primarily on concentrations, on the turbulence levels, and on the (con-stant) radii of the sediments and ice. Eciency of the aggregation process is estimated from the model and experimental results, and the "aggregation" factor is found to be 0.025.

has a signature raw state, deﬁned either through the aggregation process or the disaggregation process. Deﬁnition 1 (Anchor State). A state iis called an “aggregation anchor state” of the meta-state k if Uik = 1 and Uis = 0 for all s6= k. A state jis called a “disaggregation anchor state” of the

Parameter estimation of Gaussian stationary processes 407 X t,wecannotethatthevariableϕ(t)iscenteredgaussian,hencetheexpected value of ϕ(t)iszeroanditsvarianceisE[ϕ(t)2]=V(θ 0),asfollows: E[ϕ(t)2]= L q,q =0 a qa q E[X t−αqX t−αq] L q,q =0 a qa q ρ θ 0 (αq−q )L k=0 b kρ θ 0 (αk)=V(θ0). Following the notation of [36] we deﬁne the vector of

APPROXIMATION AND PARAMETER ESTIMATION PROBLEMS FOR ALGAL AGGREGATION MODELS ... phytoplankton populations and it is possible that the aggregation process could be ... on the parameter [3. ...

2.2 Estimation for Vasicek Process The Vasicek process satis es the univariate stochastic di erential equation dXt = ( Xt)dt+˙dB(t): (2.3) It is the Ornstein-Uhlenbeck process and was proposed by Vasicek (1977) for interest rate dynamics. The conditional distribution of Xt given Xt 1 is XtjXt 1 ˘ N Xt 1e + (1 e ); 1 2 ˙2 1(1 e 2 ) 5

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 ...

7.4 ESTIMATION OF GROWTH PARAMETERS. The least squares method (non-linear regression) allows the estimation of the parameters K, L ∞ and t o of the individual growth equations. The starting values of K, L ∞ and t 0 for the iterative process of estimation can be obtained by simple linear regression using the following methods:

Furthermore, we extend this result to general aggregation equations with a bounded Lipschitz interaction field. In this article, we study the parameter estimation of interacting particle systems subject to the Newtonian aggregation and Brownian diffusion. Specifically, we construct an estimator $\widehat{\nu}$ with partial observed data to ...

maximization (EM), which simultaneously estimate the true labels and parameters related to the annotation process such as worker reliability and task difficulty [53]. Dawid and Skene [10] make a seminal contribution by proposing to model the worker’s reliability with a confusion matrix for answers aggregation. Demartini et al.

Module 3: Gaussian Process Parameter Estimation, Prediction Uncertainty, and Diagnostics Jerome Sacks and William J. Welch National Institute of Statistical Sciences and University of British Columbia Adapted from materials prepared by Jerry Sacks and Will Welch for various short courses

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