## What is/are Parametric Identification?

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.^{[1]}The problem of parametric identification of interval discrete dynamic models is considered in the article.

^{[2]}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.

^{[3]}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.

^{[4]}We cover core models, alternative data settings, common estimation approaches, the role and choice of instruments, and nonparametric identification.

^{[5]}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.

^{[6]}Confidence intervals of parametric identification estimates are determined using the covariance matrix of parameters estimate errors and quantile χ2 - probability distribution 1-α.

^{[7]}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.

^{[8]}The proposed algebraic parametric identification techniques are based on operational calculus of Mikusiński and differential algebra.

^{[9]}The parametric identification capabilities of the proposed methods are verified via numerical and experimental tests.

^{[10]}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.

^{[11]}The parametric identification of the saponification experimental process is performed as a multi-stage stationary reaction with linear kinetics, characterized by adequate model estimates.

^{[12]}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.

^{[13]}Control decisions are made when the use of nonparametric identification and control algorithms.

^{[14]}To this purpose, a system for the parametric identification of a staircase is proposed in this article.

^{[15]}Methods of parametric identification for determination of fuel consumption are worked out and the analysis of flight technical characteristics of the helicopter is developed.

^{[16]}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.

^{[17]}This article introduces the command bunching, which implements new non-parametric and semi-parametric identification methods for estimating elasticities developed by Bertanha et al.

^{[18]}This determines the high importance of an effective solution to the problem of parametric identification.

^{[19]}This work presents a nonparametric identification method to study the nonlinear response of a micro-electromechanical system (MEMS) resonator.

^{[20]}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.

^{[21]}In this work, the parametric identification of a multi-input multi-output (MIMO) four tank system is investigated.

^{[22]}This paper establishes a novel nonparametric identification of all the unspecified elements.

^{[23]}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.

^{[24]}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.

^{[25]}Our main assumptions for nonparametric identification include monotonicity of the regression function, independence of the regression error, and completeness of the measurement error distribution.

^{[26]}And for parametric identification, i.

^{[27]}In addition, structural and parametric identification of dynamic models of sensor signal drifts is performed.

^{[28]}Nonparametric identification of the fractional probability weight (FPW) function is achieved via a partial completeness assumption.

^{[29]}The paper proposes a two-step method for structural-parametric identification of hyperbolic functions with additive noise.

^{[30]}Vector fitting is modified in this study for the parametric identification of a model with an undamped rigid body mode in the frequency domain.

^{[31]}The new parametric identification approach exhibits excellent agreement with the other methods.

^{[32]}This paper provides a systematic approach to semiparametric identification that is based on statistical information as a measure of its "quality".

^{[33]}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.

^{[34]}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.

^{[35]}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.

^{[36]}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.

^{[37]}The problem of bias of OLS estimates arises when solving the problem of parametric identification of distributed dynamic processes.

^{[38]}The structural and parametric identification of the macromodel for energy consumption in the OGPE energy complex into the generalized Golden section metric is performed.

^{[39]}The main purpose of the aircraft system identification is to estimate the aerodynamic force and torque coefficients using parametric identification approaches and intelligent modeling.

^{[40]}A method of parametric identification of the model based on the ideas of the theory of comparative identification has been developed.

^{[41]}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.

^{[42]}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.

^{[43]}I first establish nonparametric identification and estimation of all the unspecified elements and provide estimators' asymptotic properties.

^{[44]}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.

^{[45]}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.

^{[46]}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.

^{[47]}It allows a primary analysis of the hub operation and does not need large statistical information for parametric identification.

^{[48]}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.

^{[49]}Two methods were proposed to the parametric identification in this study.

^{[50]}

## parametric identification method

This article introduces the command bunching, which implements new non-parametric and semi-parametric identification methods for estimating elasticities developed by Bertanha et al.^{[1]}This work presents a nonparametric identification method to study the nonlinear response of a micro-electromechanical system (MEMS) resonator.

^{[2]}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.

^{[3]}We develop a nonparametric identification method for nonlinear gradient-flow dynamics.

^{[4]}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.

^{[5]}A new dynamic feedback tracking control method of desired velocity and current profiles for permanent magnet synchronous motors, without the additional synthesis of disturbance observers and parametric identification methods, is introduced.

^{[6]}In this paper, the parametric identification method is adopted to identify the online value of

^{[7]}It is based on theoretical results concerning nonparametric identification method, achieved for the last four decades.

^{[8]}To mitigate the influence of grid impedance on the converter impedance measurement, a MIMO parametric identification method is proposed for the direct measurement of the converter impedance matrix with a single-measurement.

^{[9]}The proposed method shows a significant simulation computational saving, compared to other parametric identification methods, which is illustrated by the means of a numerical example.

^{[10]}

## parametric identification result

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.^{[1]}For these two strategies, we give non-parametric and semi-parametric identification results without modeling assumptions on the outcome.

^{[2]}This paper provides nonparametric identification results for a class of latent utility models with additively separable unobservable heterogeneity.

^{[3]}The results potentially extend the existing non-parametric identification result for first-price sealed-bid auctions with symmetric affiliation.

^{[4]}Finally, I will consider the implications of the nonparametric identification results provided for a narrow, but non-trivial, set of causal estimands in Theorems 7 and 8.

^{[5]}

## parametric identification algorithm

From that, the parametric identification algorithm is implemented.^{[1]}Possibilities of a software package that has the most complete set of parametric identification algorithms today are discussed.

^{[2]}Real-time structural and parametric identification algorithms have been developed, which is a combination of the algorithm for identifying the coefficients of linear equations and the method of the theory of interactive adaptation.

^{[3]}Although significant research efforts during the last two decades have resulted in an extensive number of parametric identification algorithms, most of them are certainly not directly applicable for modal parameter extraction.

^{[4]}

## parametric identification problem

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.^{[1]}This paper concerns the nonparametric identification problem for a class of nonlinear discrete-time dynamical systems that is characterized by its cascade structure.

^{[2]}First, the time-varying structural physical parameters are expanded at multi-scale profile by wavelet multiresolution analysis and a time-varying parametric identification problem can be converted into a timeinvariant one.

^{[3]}The non-parametric identification problem aims to estimate a suitable model based on the response produced by a given stimulus on an uncertain model.

^{[4]}

## parametric identification tool

CONCLUSIONS CBRA makes it easy for clinicians to use modeling and parametric identification tools to reconstruct release curves.^{[1]}The proposed method uses both parametric and nonparametric identification tools.

^{[2]}This add-on requires suitable stack models, parametric identification tools and diagnostic algorithms to be run on low-cost embedded systems, ensuring a good trade-off between accuracy and computation time.

^{[3]}

## parametric identification procedure

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.^{[1]}Considering that practically the full physics of the system is often inaccessible to be modeled accurately for lack of prior knowledge of some physical/structural parameters, this paper presents a novel parametric contact model and the corresponding parametric identification procedure for steady-state speed prediction of a typical standing wave linear ultrasonic motor utilizing longitudinal and bending modes.

^{[2]}Based on a small set of assumptions on preferences, Kerschbamer (2015) introduces a geometric delineation of distributional preferences and a parsimonious, non-parametric identification procedure — the Equality Equivalence Test ( eet ).

^{[3]}

## parametric identification technique

The proposed algebraic parametric identification techniques are based on operational calculus of Mikusiński and differential algebra.^{[1]}In this paper, a nonparametric identification technique to estimate second-order plus time delay (SOPTD) processes from step response is proposed, named NMIE.

^{[2]}Parametric Identification Technique has been done for the modelling of the Reactor section.

^{[3]}

## parametric identification strategy

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.^{[1]}Nonparametric identification strategy is employed to capture causal relationships without imposing any variant of monotonicity existing in the nonseparable nonlinear error model literature.

^{[2]}

## parametric identification assumption

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.^{[1]}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.

^{[2]}

## parametric identification approach

The new parametric identification approach exhibits excellent agreement with the other methods.^{[1]}The main purpose of the aircraft system identification is to estimate the aerodynamic force and torque coefficients using parametric identification approaches and intelligent modeling.

^{[2]}