Introduction to Parametric Identification

This paper studies the nonparametric identification and estimation of projected pricing kernels implicit in European option prices and underlying asset returns using conditional moment restrictions.

The problem of parametric identification of interval discrete dynamic models is considered in the article.

Parametric identification of bridges using instrumented vehicles can be challenging, mainly due to the reduced length of the time series associated with the bridge span under test.

Paper presents the problem of parametric identification of processes of technological thermophysics based on the solution of inverse heat conduction problems under conditions of random disturbances.

We cover core models, alternative data settings, common estimation approaches, the role and choice of instruments, and nonparametric identification.

The parametric identification of the model is performed using the dependencies known from the scientific papers, and the transition matrices are aligned with the physical parameters of the mass flows, which makes the proposed model nonlinear.

Confidence intervals of parametric identification estimates are determined using the covariance matrix of parameters estimate errors and quantile χ2 - probability distribution 1-α.

Studies were based on an interdisciplinary approach to determining the structure of a human control system, as well as a class of models from the theory of automata necessary to implement both structural and parametric identification.

The proposed algebraic parametric identification techniques are based on operational calculus of Mikusiński and differential algebra.

The parametric identification capabilities of the proposed methods are verified via numerical and experimental tests.

The Equivalent Score (ES) method is a regression-based normative/standardization technique that relies on the non-parametric identification of the observations corresponding to the outer and inner tolerance limits (oTL; iTL) — to derive a cut-off, as well as to between-ES thresholds — to mark the passage across different levels of ability.

The parametric identification of the saponification experimental process is performed as a multi-stage stationary reaction with linear kinetics, characterized by adequate model estimates.

The report is devoted to the study of the problem of parametric identification of controlled dynamic systems in their normal operation mode using a model with adjustable parameters.

Control decisions are made when the use of nonparametric identification and control algorithms.

To this purpose, a system for the parametric identification of a staircase is proposed in this article.

Methods of parametric identification for determination of fuel consumption are worked out and the analysis of flight technical characteristics of the helicopter is developed.

This type of modeling can be a useful tool for the initial determination of parameters included in the TF associated with the EM, preceding advanced parametric identification procedures, e.

This article introduces the command bunching, which implements new non-parametric and semi-parametric identification methods for estimating elasticities developed by Bertanha et al.

This determines the high importance of an effective solution to the problem of parametric identification.

This work presents a nonparametric identification method to study the nonlinear response of a micro-electromechanical system (MEMS) resonator.

Synthesizing what is known about the cross-world independence assumption, we discuss the relationship between assumptions for causal mediation analyses, causal models, and nonparametric identification of natural direct and indirect effects.

In this work, the parametric identification of a multi-input multi-output (MIMO) four tank system is investigated.

This paper establishes a novel nonparametric identification of all the unspecified elements.

The use of a cognitive approach based on the construction, structural-parametric identification, and research of fuzzy cognitive maps (FCM) for these purposes is constrained by the complexity of time factor accounting.

For application of a parametric identification method this would require estimating a large number of parameters, as well as an appropriate model order selection step for a possibly large scale MISO problem, thereby increasing the computational complexity of the identification algorithm to levels that are beyond feasibility.

Our main assumptions for nonparametric identification include monotonicity of the regression function, independence of the regression error, and completeness of the measurement error distribution.

And for parametric identification, i.

In addition, structural and parametric identification of dynamic models of sensor signal drifts is performed.

Nonparametric identification of the fractional probability weight (FPW) function is achieved via a partial completeness assumption.

The paper proposes a two-step method for structural-parametric identification of hyperbolic functions with additive noise.

Vector fitting is modified in this study for the parametric identification of a model with an undamped rigid body mode in the frequency domain.

The new parametric identification approach exhibits excellent agreement with the other methods.

This paper provides a systematic approach to semiparametric identification that is based on statistical information as a measure of its "quality".

We propose new non-parametric and semi-parametric identification assumptions on the distribution of agents that are weaker than assumptions currently made in the literature.

The main contribution and advantage of the proposed new method is the identification of an interval fuzzy model in an online manner, which means that the structural and parametric identification of nonlinear systems is done simultaneously and from the data stream.

The algorithm is adaptive and is based on the parametric identification of the mathematical model of the controlled object, performed by the control system in real time, and also on the use of an implicit reference model.

A multi-step procedure is presented, consisting first in the non-parametric identification of a frequency dependent, two degrees of freedom model instrument frame by means of a polynomial rational function, where polynomial order and parameters, such as polynomial coefficients and pole-residue couples, are optimally identified by means of an algebraic numerical technique and of an iterative stabilization procedure.

The problem of bias of OLS estimates arises when solving the problem of parametric identification of distributed dynamic processes.

The structural and parametric identification of the macromodel for energy consumption in the OGPE energy complex into the generalized Golden section metric is performed.

The main purpose of the aircraft system identification is to estimate the aerodynamic force and torque coefficients using parametric identification approaches and intelligent modeling.

A method of parametric identification of the model based on the ideas of the theory of comparative identification has been developed.

To solve the arising problems of structural and parametric identification, as a rule, methods, and algorithms of the theory of adaptive control systems are used.

The problem of passive parametric identification of systems with distributed parameters for resource accumulation dynamics of many households using a stochastic distributed model in the form of a state space with regard to the white noise of the dynamics model of the object under study and the white noise of the model of a linear-type measuring system is considered in the paper.

I first establish nonparametric identification and estimation of all the unspecified elements and provide estimators' asymptotic properties.

Later, two identification strategies are selected and adapted to our case: set membership, a data-driven, nonlinear and non-parametric identification strategy which needs no input redefinition; and Recursive least-squares (RLS), a widely recognized identification technique.

A method of structural-parametric identification based on experimental logarithmic magnitude-frequency characteristics is proposed which will allow for the same set of experimental points to select the structure of the mathematical model of varying complexity depending on the specified accuracy.

A method of structural-parametric identification was developed for the problem of object simulation with a multidimensional output in the class of beta-autoregressive models, in which autoregrassion weight coefficient ratios are determined based on beta-distribution density functions.

It allows a primary analysis of the hub operation and does not need large statistical information for parametric identification.

Drawing on experimental data, a structural and parametric identification of the Hammerstein, Wiener and Hammerstein-Wiener models with a polynomial structure of the linear dynamic block and piecewise linear static nonlinearities was performed.

Two methods were proposed to the parametric identification in this study.

The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.

This paper addresses the issue of parametric identification of a DC motor with independent excitation.

The research analyzes scientific and methodological approaches for structural and parametric identification of fuzzy cognitive models based on time-dependent functional dependencies.

To ensure the stability of the water treatment process in the statistical sense, a procedure for parametric identification of the predictive model from a limited sample is proposed.

Most Data Mining techniques are based on traditional methods of constructing mathematical models and their parametric identification.

A method of structural and parametric identification of this interval model which are based on the behavioral model of artificial bee colony is proposed.

Inverse problem of the Lorenz system parametric identification is considered in the case of incomplete information about solutions of the system.

To ensure the quantitative reliability of the calculated forecasts, a parametric identification of the model was performed using known empirical dependencies to calculate the particle resistance coefficient and estimate the coefficient of their macrodiffusion.

We develop a nonparametric identification method for nonlinear gradient-flow dynamics.

We propose in this paper a nonparametric identification system addressing these main goals.

An inappropriate sensor placement of ALLS may result in inaccurate flow-field parametric identification.

In this paper, a nonparametric identification technique to estimate second-order plus time delay (SOPTD) processes from step response is proposed, named NMIE.

CONCLUSIONS CBRA makes it easy for clinicians to use modeling and parametric identification tools to reconstruct release curves.

This paper establishes a novel nonparametric identification of all the unspecified elements.

The model enjoys ease of parametric identification from macroscopic mechanical response over physically-based models.

I propose a flexible yet tractable sequential auction model under the private value paradigm and establish its nonparametric identification, demonstrating an intuitive and general method for disentangling synergy from affiliation.

From that, the parametric identification algorithm is implemented.

The problem of calibration of measuring instruments for given conditions based on the correction function is considered as a measurement problem of structural-parametric identification of the calibration diagram.

In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data.

For these two strategies, we give non-parametric and semi-parametric identification results without modeling assumptions on the outcome.

We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain.

In this article, nonparametric identification of nonlinear autoregressive systems with exogenous inputs (NARX) is considered; a general criterion function is introduced for estimating the value of the nonlinear function within the system at any fixed point.

We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved choice sets.

This condition complicates significantly the solution search of non-parametric identification problems in the system because an output of one subsystem is an input of another subsystem, so active identification schemes are unappropriated.

the minimum inertia needed at each network bus, in order to ensure the transient stability of the power system, and describes a methodology based on parametric identification for its assessment.

Finally, a simulation example based on parametric identification is provided to highlight the feasibility of the suggested method.

This paper proposes a robust method for semiparametric identification and estimation in panel multinomial choice models, where we allow for infinite-dimensional fixed effects that enter into consumer utilities in an additively nonseparable way, thus incorporating rich forms of unobserved heterogeneity.

With the parametric identification of the DC motor, it is possible to approximate the dynamics of the manipulator.

In this paper, the application of non-parametric identification method using feedforward neural networks (FNNs) to model a flexible beam structure for AVC system is presented.

To analyze stability of valves operation and to calculate oscillatory processes, the dynamic model is required, the parametric identification of which has been accomplished with the use of experimental data and multidimensional numerical simulation results.