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- Paperback: 328 pages
- Publisher: Cambridge University Press (August 26, 2002)
- Language: English
- ISBN: 0521002559
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Structural Equation Modeling And Regression Guidelines For Research Practice
Abstract: The growing interest in Structured Equation Modeling (SEM) techniques and recognition of their importance in IS research suggests the need to compare and contrast different types of SEM techniques so that research designs can be appropriately selected. After assessing the extent to which these techniques are currently being used in IS research, the article presents a running example which analyzes the same dataset via three very different statistical techniques
[此贴子已经被作者于2006-4-10 8:24:51编辑过]
Switching State-Space Models
Zoubin Ghahramani and Geoffrey E. Hinton
Gatsby Computational Neuroscience Unit
University College London
London WC1N 3AR, U.K.
We introduce a new statistical model for time series which iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time series models---hidden Markov models and linear dynamical systems---and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network (Jacobs et al, 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact Expectation Maximization (EM) algorithm cannot be applied. However, we present a variational approximation that maximizes a lower bound on the log likelihood and makes use of both the forward--backward recursions for hidden Markov models and the Kalman filter recursions for linear dynamical systems. We tested the algorithm both on artificial data sets and on a natural data set of respiration force from a patient with sleep apnea. The results suggest that variational approximations are a viable method for inference and learning in switching state-space models.
Extended version of Technical Report CRG-TR-96-3 (1996)
Decisionmetrics: A Decision-Based Approach to Econometric Modelling
Abstract:
In many applications it is necessary to use a simple and therefore highly misspecified econometric model as the basis for decision-making. We propose an approach to developing a possibly misspecified econometric model that will be used as the beliefs of an objective expected utility maximiser. A discrepancy between model and 'truth' is introduced that is interpretable as a measure of the model's value for this decision-maker. Our decision-based approach utilises this discrepancy in estimation, selection, inference and evaluation of parametric or semiparametric models. The methods proposed nest quasi-likelihood methods as a special case that arises when model value is measured by the Kullback-Leibler information discrepancy and also provide an econometric approach for developing parametric decision rules (e.g. technical trading rules) with desirable properties. The approach is illustrated and applied in the context of a CARA investor's decision problem for which analytical, simulation and empirical results suggest it is very effective.
[此贴子已经被作者于2006-4-10 8:38:31编辑过]
Statistical Case Studies
Wolfgang Hardle, Yuichi Mori, Philippe Vieu
[此贴子已经被作者于2006-4-10 8:47:56编辑过]
Statistical Inference in the Multinominal Multiperiod Probit Model
Authors: Geweke J.F.1; Keane M.P.; Runkle D.E.
Source: Journal of Econometrics, Volume 80, Number 1, September 1997, pp. 125-165(41)
Publisher:Elsevier Science
[此贴子已经被作者于2006-4-10 8:56:06编辑过]
Process Control : A First Course with MATLAB
Book Description
Process Control covers the most essential aspects of process control suitable for a one-semester introductory course. While classical techniques are discussed, Chau also covers state space modeling and control, a modern control topic lacking in most introductory texts. MATLAB, a popular engineering software package, is employed as a powerful yet approachable computational tool. Each chapter concludes with problem sets, to which hints or solutions are provided. The support website provides excellent support in the way of MATLAB outputs of text examples and MATLAB sessions, references, and supplementary notes
Product Details
[此贴子已经被作者于2006-4-10 23:31:35编辑过]
John L. Kling
Abstract
Standard linear least-squares prediction methods are not directly applicable to making probability statements about time series turning points. William F. Wecker suggested a method for extending the least-squares technique to allow computation of the probability distribution of turning points of a time series. Wecker's analysis was univariate and did not consider all sources of uncertainty (i.e., estimates of coefficients). The primary purpose of this paper is fourfold: (1) to extend Wecker's analysis to the case of the multiple time-series model; (2) to consider most sources of model uncertainty; (3) to test the procedure for reliability (method of calibrations); and (4) to demonstrate some interesting applications. Copyright 1987 by University of Chicago Press.
| Publisher Info |
Volume (Year): 60 (1987)
Issue (Month): 2 (April)
Pages: 201-38
Understanding Neural Networks as Statistical Tools
Brad Warner, Manavendra Misra
Abstract: Neural networks have received a great deal of attention over the last few years. They are being used in the areas of prediction and classification; areas where regression models and other related statistical techniques have traditionally been used. In this paper, we discuss neural networks and compare them to regression models. We start by exploring the history of neural networks. This includes a review of relevant literature on the topic of neural networks. Neural network nomenclature is then
[此贴子已经被作者于2006-4-11 7:48:20编辑过]
Quantitative Data Analysis for Human Geographers Using SPSS
[此贴子已经被作者于2006-4-14 20:47:19编辑过]
Introduction to Bayesian Statistics
William M. Bolstad
ISBN: 0-471-27020-2
Hardcover
376 pages
April 2004
CDN $128.99
[此贴子已经被作者于2006-4-16 2:01:01编辑过]
Forecasting economic and financial time-series with non-linear models
Michael P. Clements (University of Warwick)
Philip Hans Franses (Erasmus University Rotterdam.)
Norman R. Swanson (
In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.
[此贴子已经被作者于2006-4-17 8:51:54编辑过]
Student Solutions Manual to Comapny Applied Linear Statistical Models
[此贴子已经被作者于2006-4-20 11:11:14编辑过]
Applied Linear Statistical Models
John Neter, Michael H Kutner, William Wasserman, Christopher J. Nachtsheim, John Neter
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[此贴子已经被作者于2006-4-20 11:08:44编辑过]
J. Scott Long
Sage
1997
那里有这本书啊
[此贴子已经被作者于2006-4-21 12:57:44编辑过]
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[此贴子已经被作者于2006-4-22 23:50:02编辑过]
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