Showing papers on "Parametric statistics published in 1988"

Adaptive manipulator control: A case study

Abstract: The author's previous work (1986, 1987) utilized the particular structure of manipulator dynamics to develop a simple, globally convergent adaptive controller for manipulator trajectory control problems. After summarizing the basic algorithm, they demonstrate the approach on a high-speed two-degree-of-freedom semi-direct-drive robot. They show that the dynamic parameters of the manipulator, assumed to be initially unknown, can be estimated within the first half second of a typical run, and that accordingly, the manipulator trajectory can be precisely controlled. These experimental results demonstrate that the adaptive controller enjoys essentially the same level of robustness to unmodeled dynamics as a PD (proportional and differential) controller, yet achieves much better tracking accuracy than either PD or computed-torque schemes. Its superior performance for high-speed operations, in the presence of parametric and nonparametric uncertainties, and its relative computational simplicity, make it an attractive option both for addressing complex industrial tasks, and for simplifying high-level programming of more standard operations. >

Inference in Nonlinear Econometric Models with Structural Change

Abstract: This paper extends the classical test for structural change in linear regression models (see Chow (1960)) to a wide variety of nonlinear models, estimated by a variety of different procedures. Wald, Lagrange multiplier-like, and likelihood ratio-like test statistics are introduced. The results allow for heterogeneity and temporal dependence of the observations. In the process of developing the above tests, the paper also provides a compact presentation of general unifying results for estimation and testing in nonlinear parametric econometric models.

Variation of geometrics based on a geometric-reasoning method

Abstract: A method is described for processing generic geometric models on the basis of symbol manipulation and inferencing. A two-dimensional model is represented as a set of geometric elements and a set of atomic formulas that define the constraining scheme. Processing of a model is performed by a rule-based system, whose rules embody the general knowledge for the propagation of constraining information in geometric structures. Forward inferencing is used as an example of a possible control strategy. The system detects whether or not a model description is consistent and derives a plan for the construction of variants. Given a numerical value for each parametric constraint of a model, a variant is rapidly generated through automatic plan execution.

Chi-Square Diagnostic Tests for Econometric Models: Theory

Abstract: This paper extends the Pearson chi-square testing method to nondynamic parametric econometric models, in particular, to models with covariates. The paper establishes the asymptotic distribution of the test statistic under the null and local alternatives when the test statistic is based on data-dependent random cells of a general form and on an arbitrary asymptotically normal estimator. These results are attained by extending recent probabilistic results for the weak convergence of empirical processes indexed by sets. The chi-square test that is introduced can be used to test goodness-of-fit of a parametric model, as well as to test particular aspects of the parametric model that are of interest.

A parametric method of identification of single-trial event-related potentials in the brain

Abstract: A parametric method of identification of event-related (or evoked) potentials on a single-trial basis through an ARX (autoregressive with exogenous input) algorithm is discussed. The basic estimation of the information contained in the single trial is taken from an average carried out on a sufficient number of trials, while the noise sources, EEG and EOG, are characterized as exogenous inputs in the model. The simulations as well as the experimental results confirm the capability of the model of drastically improving the S/N (signal-to-noise) ratio in each single trial and satisfactorily identifying the contributions of signal and noise to the overall recording. A particularly efficient reduction of ocular artifacts is also achieved. >

Automatic pattern recognition: a study of the probability of error

Abstract: A test sequence is used to select the best rule from a class of discrimination rules defined in terms of the training sequence. The Vapnik-Chervonenkis and related inequalities are used to obtain distribution-free bounds on the difference between the probability of error of the selected rule and the probability of error of the best rule in the given class. The bounds are used to prove the consistency and asymptotic optimality for several popular classes, including linear discriminators, nearest-neighbor rules, kernel-based rules, histogram rules, binary tree classifiers, and Fourier series classifiers. In particular, the method can be used to choose the smoothing parameter in kernel-based rules, to choose k in the k-nearest neighbor rule, and to choose between parametric and nonparametric rules. >

The simultaneous solution and sensitivity analysis of systems described by ordinary differential equations

Abstract: The methodology for the simultaneous solution of ordinary differential equations and the associated first-order parametric sensitivity equations is presented, and a detailed description of its implementation as a modification of a widely disseminated implicit ODE solver is given. The error control strategy ensures that local error criteria are independently satisfied by both the model and sensitivity solutions. The internal logic effectuated by this implementation is detailed. Numerical testing of the algorithm is reported; results indicate that greater reliability and improved efficiency is offered over other sensitivity analysis methods.

Parametric Study of Beams on Elastic Foundations

Abstract: Even though the two‐parameter model developed by Vlasov for beams on elastic foundations represents the interaction between the beams and the foundation better than the Winkler model, it requires an estimation of a third parameter, γ, which represents the distribution of the displacements within the foundation. Using variational calculus, the value of γ is shown to be a function of some nondimensional parameters of the beam, the foundation, and the mode of loading. By using an iterative procedure, a consistent value of γ is calculated. An example of a beam carrying a uniformly distributed load is solved and the results are compared with a more rigorous finite element solution.

20 Nonparametric methods for changepoint problems

Abstract: Publisher Summary Changepoint problems have originally arisen in the context of quality control, where one typically observes the output of a production line and would wish to signal deviation from an acceptable average output level while observing the data. When one observes a random process sequentially and stops observing at a random time of detecting change, then one speaks of a sequential procedure. Otherwise, it is observed that a large finite sequence for the sake of determining possible change during the data collection. Such procedures are described in terms of asymptotic results, and can be called as nonsequential procedures. Sequential and nonsequential procedures are usually based on parametric or nonparametric models for changepoint problems, allowing at most one change (AMOC) or, possibly, more than one change. This chapter focuses on the nonparametric AMOC setting and discusses non-sequential nonparametric AMOC procedures. A large number of nonparametric and parametric modelling of AMOC problems result in the same test statistic, general rank statistics with quantile and Wilcoxon type scores whose asymptotics are described in terms of a two-time parameter stochastic process, U-statistics type processes which are considered for the nonparametric AMOC problem, and detect change in the intensity parameter of a renewal process.

Semiparametric econometrics: A survey

Abstract: SUMMARY Semiparametric econometric models contain both parametric and nonparametric components, reflecting in some fashion what has been learned from economic theory and previous empirical experience, and what remains unknown. They raise such questions as how well the parametric component can be estimated, and how to construct rules of inference with good statistical properties. The paper attempts to survey the econometric and most relevant statistical literature on semiparametric inference, and includes a partial bibliography.

Parametric Overloading in Polymorphic Programming Languages

Abstract: The introduction of unrestricted overloading in languages with type systems based on implicit parametric polymorphism generally destroys the principal type property: namely that the type of every expression can uniformly be represented by a single type expression over some set of type variables. As a consequence, type inference in the presence of unrestricted overloading can become a NP-complete problem. In this paper we define the concept of parametric overloading as a restricted form of overloading which is easily combined with parametric polymorphism. Parametric overloading preserves the principal type property, thereby allowing the design of efficient type inference algorithms. We present sound type deduction systems, both for predefined and programmer defined overloading. Finally we state that parametric overloading can be resolved either statically, at compile time, or dynamically, during program execution.

Error Of Fit Measures For Recovering Parametric Solids

Abstract: Parametric models of objects are becoming increasingly more impor- tant in computer vision. In the past few years, a number of researchers have investigated the recovery of a class of parametric models by the minimization of an "error of fit" measure. The measures used have typ- ically been chosen in an ad hoc fashion. This paper looks at how these measures affect the performance of a recovery system. This research can be divided into two parts. The first studies the biases of the po- tential error-of-fit measures with respect to the parameters recovered and examines the cross-sectional shape of their respective "error of fit" surfaces. This study is done in simulation by holding all but one pa- rameter constant. The second part of the research compares two of the better "error of fit" measures by using them in a recovery system. Both the number of iterations and the quality of the reconstruction are considered.

Displaying method and apparatus for three-dimensional computer graphics

Abstract: A display apparatus and a modelling method for computer graphics includes a first memory for storing a first three-dimensional model, a detailed model, of a body represented by at least one of desired first number of dimensions, a desired first number of parameters and desired first parametric quantities, an arithmetic unit for arithmetically determining a second three-dimensional model, a simplified model, by varying at least one of the first number of dimensions, the first number of parameters and the first parametric quantities to at least one of a second number of dimensions, a second number of parameters and second parametric quantities in accordance with an algorithm for thereby creating automatically a simplified model for a body of less importance. A second memory stores the simplified model. A selector selects the first three-dimensional model or the second three-dimensional model by reference to a preset index for evaluation. A display unit displays the first three-dimensional model or the second three-dimensional model as selected.

Geometric continuity, shape parameters, and geometric constructions for Catmull-Rom splines

Abstract: Catmull-Rom splines have local control, can be either approximating or interpolating, and are efficiently computable. Experience with Beta-splines has shown that it is useful to endow a spline with shape parameters, used to modify the shape of the curve or surface independently of the defining control vertices. Thus it is desirable to construct a subclass of the Catmull-Rom splines that has shape parameters.We present such a class, some members of which are interpolating and others approximating. As was done for the Beta-spline, shape parameters are introduced by requiring geometric rather than parametric continuity. Splines in this class are defined by a set of control vertices and a set of shape parameter values. The shape parameters may be applied globally, affecting the entire curve, or they may be modified locally, affecting only a portion of the curve near the corresponding joint. We show that this class results from combining geometrically continuous (Beta-spline) blending functions with a new set of geometrically continuous interpolating functions related to the classical Lagrange curves.We demonstrate the practicality of several members of the class by developing efficient computational algorithms. These algorithms are based on geometric constructions that take as input a control polygon and a set of shape parameter values and produce as output a sequence of Bezier control polygons that exactly describes the original curve. A specific example of shape design using a low-degree member of the class is given.

Parametric yield optimization for MOS circuit blocks

Abstract: Two techniques are presented for optimizing the parametric yield of digital MOS circuit blocks for VLSI designs. The first is based on quasi-Newton methods and utilizes the gradient of the yield. A novel technique for computing this yield gradient is derived and algorithms for its implementation are discussed. Geometrical considerations motivate the second method which formulates the problem in terms of a minimax problem. Both yield optimization techniques utilize transient sensitivity information from circuit simulations. Encouraging results have been obtained thus far; several circuit examples are included to demonstrate these techniques. >

The modeling of batch and continuous emulsion polymerization reactors. Part I: Model formulation and sensitivity to parameters

Abstract: A detailed model for the prediction of the behavior of batch or continuous emulsion polymerization reactors has been formulated, and an efficient numerical scheme for simulation developed. The model makes use of population balance equations and detailed mechanisms for chemical and physical rate processes. The numerical procedure chosen for its solution is orthogonal collocation on finite elements. In this paper, a few comparisons with experimental data are presented to demonstrate the model validity. Finally, a parametric sensitivity study is carried out to identify the most important kinetic and physical parameters. In the sequel, a comprehensive comparison of model predictions with a wide variety of experimental data will be presented.

Utility-efficient programming for whole-farm planning

Abstract: A programming technique, utility-efficient programming, is developed for farm planning under risk. The objective function is the parametric sum of two parts of the utility function in which the degree of risk aversion varies systematically with the parameter. This technique has several advantages over those previously available: a number of types of utility functions are applicable including ones exhibiting decreasing risk aversion; the degree of risk aversion can be limited to a plausible range; the form of the distribution for activity net revenues is flexible; and the technique can be used with available algorithms. The method is illustrated using a parametric linear programming algorithm.

Robustness in the Presence of Joint parametric Uncertainty and Unmodeled Dynamics

Abstract: It is shown that, in the case of joint real parametric and complex uncertainty, Doyle's structured singular value can be obtained as the solution of a smooth constrained optimization problem. While this problem may have local maxima, an improved computable upper bound to the structured singular value is derived, leading to a sufficient condition for robust stability and performance.

Efficiency Analysis by Production Frontiers: The Nonparametric Approach

Abstract: 1 Efficiency Analysis in Production- 11 Partial and General Equilibrium Models- 12 Production Frontier as Flexible Production Functions- 13 Parametric Forms and their Econometric Estimation- 14 Nonparametric Theory: Different Facets- 15 Implications of Nonparametric Theory- 2 The Nonparametric Approach- 21 Convex Hull Method- 22 Stochastic Micro and Macro Frontier- 23 Data Envelopment Analysis- 24 Consistency Approach through Data Adjustment- 25 Distribution of Technical and Price Efficiency- 3 Interface With Parametric Theory- 31 Average versus Optimal Production Function- 32 Estimation of Production Frontier- 33 Robust Methods of Estimation- 34 Data Screening and Cluster Analysis- 35 Problems in Parametric Theory- 4 Implications of Nonparametric Theory- 41 Economic Implications- 42 Econometrics and Minimax Frontiers- 43 The Regression and Index Number Problem- 44 Joint Costs and Nonlinear Frontiers- 45 Efficiency Analysis in the Stochastic Case- 5 Extensions of Nonparametric Approach- 51 Economic Generalizations- 52 Extensions of Data Envelopment Analysis- 53 Combining Parametric and Nonparametric Models- 54 Nonparametric Regression and Bootstrap Methods- 55 Distribution of Technical and Price Efficiency- 6 Applications of Nonparametric Theory- 61 Production in Quasi-Market and Non-Market Systems- 62 Managerial Models in Operations Research- 63 Economic Development and International Trade- 64 Stochastic Programming and Markov Process Models- 65 Multivariate Data Analysis and Information Theory- Author Index

Dynamic modeling of large-scale networks with application to gas distribution

Abstract: A mathematical description of dynamic processes in large scale networks is presented in this volume. The authors describe extensive practical applications and significant economical savings, and consider many interesting and difficult research problems whose successful solution is a prerequisite for the accomplishment of the whole task. The main techniques discussed are: a new integration method resistant to data systematic errors and parametric oscillations, an effective decomposition method based on the sparsity of system matrices; and the implementation of SIMONE software for simulation of the dynamic modeling of a gas transport pipeline network, including compressor stations.

Markov process model for fatigue crack growth

Abstract: Crack propagation analysis is a major task in the design and life prediction of fatigue‐critical structures, yet experimental tests indicate that fatigue crack propagation involves a large amount of statistical variation and is not adequately modeled deterministically. A method of analysis based on Markov process theory is presented for the investigation of fatigue crack propagation. A new fracture mechanics based, lognormal random process model is developed, and without approximation, a boundary value problem is formulated for the statistical moments of the random time to reach a given crack size. A Petrov‐Galerkin finite element method is then used to obtain solutions to the boundary value problem. A parametric study of the power‐law fatigue crack growth model is conducted, and a numerical example is given in which excellent agreement is found between the finite element results and experimental data. The model and problem formulation are consistent with physical phenomena, overcome many objections to pr...

Complexity results for multicriterial and parametric network flows using a pathological graph of Zadeh

Abstract: For two classes of network flow problems a worst-case analysis is given depending on the number of vertices of a pathological graph of Zadeh. Firstly, an exponential number of breakpoints in the optimal value function of the maximal flow problem in generalized networks with parametric capacities is demonstrated. Secondly, it is shown that the bicriterial min-cost flow has, in the worst case. an exponential number of efficient extreme point solutions in the objective space.