Numerically stable algorithm for cycloidal splines

We propose a knot insertion algorithm for splines that are piecewisely in L{1, x, sin x, cos x}. Since an ECC-system on [0, 2π] in this case does not exist, we construct a CCC-system by choosing the appropriate measures in the canonical representation. In this way, a B-basis can be constructed in much the same way as for weighted and tension splines. Thus we develop a corner cutting algorithm for lower order cycloidal curves , though a straightforward generalization to higher order curves, where ECC-systems exist, is more complex. The important feature of the algorithm is high numerical stability and simple implementation. This research was supported by Grant 037-1193086-2771, by the Ministry of science, higher education and sports of the Republic of Croatia.     

Asymptotically efficient parameter estimation for ordinary differential equations

Parameter estimation for ordinary differential equations arises in many fields of science and engineering. To be the best of our knowledge, traditional methods are often either computationally intensive or inaccurate for statistical inference. Ramsay et al. (2007) proposed a generalized profiling procedure. It is easily implementable and has been demonstrated to have encouraging numerical performance. However, little is known about statistical properties of this procedure. In this paper, we provide a theoretical justification of the generalized profiling procedure. Under some regularity conditions, the procedure is shown to be consistent for a broad range of tuning parameters. When the tuning parameters are sufficiently large, the procedure can be further shown to be asymptotically normal and efficient.     

Numerically stable methods for quadratic programming

Numerically stable algorithms for quadratic programming are discussed. A new algorithm is described for indefinite quadratic programming which utilizes methods for updating positivedefinite factorizations only. Consequently all the updating procedures required are common to algorithms for linearly-constrained optimization. The new algorithm can be used for the positive-definite case without loss of efficiency.     

New algorithm for optimal parameter estimation with linear constraints

This paper presents a new algorithm for optimal parameter estimation problems with linear constraints. The algorithm developed is based on least absolute-value approximations. The problem is solved first using a least-error-square technique, where we add to the cost function the equality constraints via Lagrange multipliers, to obtain a good estimate for the residuals of the measurements, having gained this information, we choose a number of measurements with the smallest residuals. This number equals the number of parameters to be estimated minus the number of constraints. Using these measurements together with the constraints, we obtain a number of observations equal to the number of parameters to be estimated. By using this technique, we show that there is no need to either iterate or use linear programming to obtain the estimation.This work was supported by the Natural Sciences and Engineering Research Council of Canada, Grant A4146.     

On stable estimation of a function of a parameter

Given the function f and the vector-statistic t_N which is a mean square consistent estimator of a parameter a, the problem is to estimate f(a). The criteria for the mean square consistency of the estimator f(t_N) are considered. In the case where the estimator f(t_N) is not mean square consistent, a class of estimators of f(a) is proposed, and it is proved that the estimators of the class are mean square consistent for all distribution of t_N. Translated fromStatisticheskie Metody Otsenivaniya i Proverki Gipotez, pp. 44–55, Perm, 1990.     

An innovative fractional order LMS algorithm for power signal parameter estimation

Parameter estimation is an important issue for the quality monitoring and reliability assessment of power systems. In this study, an innovative fractional order least mean square (I-FOLMS) adaptive algorithm is presented for an effective parameter estimation. The I-FOLMS algorithm exploits the fractional gradient in its recursive parameter update mechanism, because its performance can be tuned by means of the fractional order. High values of the fractional order are good for fast convergence, but lead to steady state mis-adjustments. While, low values provide a smooth steady state behavior, but require a compromise in the convergence rate. The effective performance of I-FOLMS is verified and validated through two numerical examples of power signals estimation for different levels of noise variance and values of the fractional orders.     

An implicit least squares algorithm for nonlinear rational model parameter estimation

In this study a new insight into least squares regression is identified and immediately applied to estimating the parameters of nonlinear rational models. From the beginning the ordinary explicit expression for linear in the parameters model is expanded into an implicit expression. Then a generic algorithm in terms of least squares error is developed for the model parameter estimation. It has been proved that a nonlinear rational model can be expressed as an implicit linear in the parameters model, therefore, the developed algorithm can be comfortably revised for estimating the parameters of the rational models. The major advancement of the generic algorithm is its conciseness and efficiency in dealing with the parameter estimation problems associated with nonlinear in the parameters models. Further, the algorithm can be used to deal with those regression terms which are subject to noise. The algorithm is reduced to an ordinary least square algorithm in the case of linear or linear in the parameters models. Three simulated examples plus a realistic case study are used to test and illustrate the performance of the algorithm.     

Regularization parameter selection and an efficient algorithm for total variation-regularized positron emission tomography

In positron emission tomography, image data corresponds to measurements of emitted photons from a radioactive tracer in the subject. Such count data is typically modeled using a Poisson random variable, leading to the use of the negative-log Poisson likelihood fit-to-data function. Regularization is needed, however, in order to guarantee reconstructions with minimal artifacts. Given that tracer densities are primarily smoothly varying, but also contain sharp jumps (or edges), total variation regularization is a natural choice. However, the resulting computational problem is quite challenging. In this paper, we present an efficient computational method for this problem. Convergence of the method has been shown for quadratic regularization functions and here convergence is shown for total variation regularization. We also present three regularization parameter choice methods for use on total variation-regularized negative-log Poisson likelihood problems. We test the computational and regularization parameter selection methods on two synthetic data sets.     

Stochastic dominance and parameter estimation: The case of symmetric stable distributions

Stochastic dominance rules can be applied to the selection of statistical estimators. This paper applies the procedure to estimators of location parameters of stable distributions: the mean and the median. It was found that the preference of one estimator over another depends on the characteristic exponent and on the sample size. Furthermore for some combinations of characteristic exponents and sample size we found that the stochastic dominance rule yields no preference implying that depending on one's utility function one may prefer the mean over the median or vise versa. This result differs from the common mean squared error criterion.     

Optimal parameter estimation for Muskingum model based on Gray-encoded accelerating genetic algorithm

In order to reduce the computational amount and improve the computational precision for parameter optimization of Muskingum model, a new algorithm, Gray-encoded accelerating genetic algorithm (GAGA) is proposed. With the shrinking of searching range, the method gradually directs to an optimal result with the excellent individuals obtained by Gray genetic algorithm (GGA). The global convergence is analyzed for the new genetic algorithm. Its efficiency is verified by application of Muskingum model. Compared with the nonlinear programming methods, least residual square method and the test method, GAGA has higher precision. And compared with GGA and BGA (binary-encoded genetic algorithm), GAGA has rapider convergent speed.     

Numerically stable deflation of hessenberg and symmetric tridiagonal matrices

While numerically stable techniques have been available for deflating a fulln byn matrix, no satisfactory finite technique has been known which preserves Hessenberg form. We describe a new algorithm which explicitly deflates a Hessenberg matrix in floating point arithmetic by means of a sequence of plane rotations. When applied to a symmetric tridiagonal matrix, the deflated matrix is again symmetric tridiagonal. Repeated deflation can be used to find an orthogonal set of eigenvectors associated with any selection of eigenvalues of a symmetric tridiagonal matrix.     

An efficient ECM algorithm for maximum likelihood estimation in mixtures of t-factor analyzers

Mixture of t factor analyzers (MtFA) have been shown to be a sound model-based tool for robust clustering of high-dimensional data. This approach, which is deemed to be one of natural parametric extensions with respect to normal-theory models, allows for accommodation of potential noise components, atypical observations or data with longer-than-normal tails. In this paper, we propose an efficient expectation conditional maximization (ECM) algorithm for fast maximum likelihood estimation of MtFA. The proposed algorithm inherits all appealing properties of the ordinary EM algorithm such as its stability and monotonicity, but has a faster convergence rate since its CM steps are governed by a much smaller fraction of missing information. Numerical experiments based on simulated and real data show that the new procedure outperforms the commonly used EM and AECM algorithms substantially in most of the situations, regardless of how the convergence speed is assessed by the computing time or number of iterations.     

Optimal inputs and sensitivities for parameter estimation

Observed data for parameter estimation is often both difficult and expensive to obtain. Thus, when an experiment is conducted, the input to the system should be such that the sensitivity to the parameter being estimated is maximized. Evaluation of the optimal inputs and sensitivities requires the solution to a two-point boundary-value problem. The method of quasilinearization is then used for parameter estimation. Numerical results are given for a simple example utilizing both optimal and nonoptimal inputs. The results clearly show the advantages of utilizing an optimal input.     

State and parameter estimation for nonlinear systems

State and parameter estimators are obtained for systems described by nonlinear evolution equations. Linear infinite dimensional observability theory together with a variety of fixed point theorems can be employed to obtain a finite time observer. Moreover, a nonlinear asymptotic observer is produced using stability results. The problem of joint state and parameter estimation is converted to the state estimation case, via an augmented state, so that these observer results can be utilised. Examples and remarks on the generality of the results are given.     

On parameter estimation for cusp-type signals

We consider the problem of parameter estimation by continuous time observations of a deterministic signal in white Gaussian noise. It is supposed that the signal has a cusp-type singularity. The properties of the maximum-likelihood and Bayesian estimators are described in the asymptotics of small noise. Special attention is paid to the problem of parameter estimation in the situation of misspecification in regularity, i.e., when the statistician supposes that the observed signal has this singularity, but the real signal is smooth. The rate and the asymptotic distribution of the maximum-likelihood estimator in this situation are described.     

An efficient algorithm for the “stable roommates” problem

The stable marriage problem is that of matching n men and n women, each of whom has ranked the members of the opposite sex in order of preference, so that no unmatched couple both prefer each other to their partners under the matching. At least one stable matching exists for every stable marriage instance, and efficient algorithms for finding such a matching are well known. The stable roommates problem involves a single set of even cardinality n, each member of which ranks all the others in order of preference. A stable matching is now a partition of this single set into n/2 pairs so that no two unmatched members both prefer each other to their partners under the matching. In this case, there are problem instances for which no stable matching exists. However, the present paper describes an O(n²) algorithm that will determine, for any instance of the problem, whether a stable matching exists, and if so, will find such a matching.     

Experimental design for distributed parameter vector systems

We formulate an optimal design problem for the selection of best states to observe and optimal sampling times and locations for parameter estimation or inverse problems involving complex nonlinear partial differential systems. An iterative algorithm for implementation of the resulting methodology is proposed.     

Procedure for asymptotic state and parameter estimation of nonlinear distributed parameter bioreactors

Due to measurement problem in biological variables, such as the biomass concentration and its specific growth rate, the on-line measurement of these variables is not ensured and this presents a great difficulty, especially when we consider the control problem. This paper presents and analyses an approach for estimating these biological variables through an example of a nonlinear distributed parameter bioreactor. An orthogonal collocation method is applied in the distributed system to obtain a system of ordinary differential equations. A simulation study shows the feasibility and the robustness of the estimator used for this nonlinear process.     

Set membership parameter estimation of fractional models based on bounded frequency domain data

This paper deals with parameter estimation of fractional models based on frequency domain uncertain but bounded data. Fractional models parameters, including differentiation orders, are expressed as intervals. Real and complex interval analysis is then used to estimate the whole set of feasible parameters. Two methods based on interval constraints satisfaction problem (CSP) are compared to each other. The first one formulates a real-CSP based on an explicit decomposition of the frequency response in the real and the imaginary parts. The second one formulates a complex-CSP using the complex frequency response without any decomposition. To have a fair comparison, rectangular representation of complex intervals is considered. Contractors combined to bisection algorithms are used to solve both CSPs.