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Mortality Forecasting for Multiple Populations: An Augmented Common Factor Model with a Penalized Log-Likelihood

Communications in Statistics Case Studies Data Analysis and Applications 2018年第3-4期4卷 118-141页

作者： Kang, Mao Liu, Yanxin Siu-Hang Li, Johnny Chan, Wai-Sum Department of Statistics and Actuarial Science University of Waterloo ON Canada Department of Finance University of Nebraska-Lincoln Lincoln NE United States Department of Economics The University of Melbourne Melbourne Australia Department of Finance The Chinese University of Hong Kong Hong Kong

Recently, the topic of multipopulation mortality forecasting has gained considerable attention among researchers and end-users. One of the most popular multipopulation mortality models is the augmented common factor (ACF) model. In spite of its popularity, the ACF model is subject to the limitation of producing mortality forecasts with a jagged pattern rather than a smooth relationship with age. In this article, we attempt to mitigate this problem by generalizing the work of Delwarde et al. [(2007). smoothing the Lee–Carter and Poisson log-bilinear models for mortality forecasting a penalized log-likelihood approach. Statistical Modelling 7 (1), 29–48] to a multipopulation setting. The generalization involves (Formula presented.) smoothing parameters, one for the common trend and one for each of the n_p individual populations. The smoothing parameters are determined by using an extended leave-one-out cross-validation. We illustrate the proposed extension with real mortality data. It is found that compared to the original ACF model, the proposed extension produces mortality forecasts that are more reasonable, with less jagged patterns across ages. © 2019, © 2019 Taylor & Francis Group, LLC.

关键词： Demographic forecasting mortality forecasting parameter smoothing stochastic modeling the Lee–Carter model

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New Approach to Noncausal Identification of Nonstationary Stochastic FIR Systems Subject to Both Smooth and Abrupt parameter Changes

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2013年第7期58卷 1847-1853页

作者： Niedzwiecki, Maciej Gackowski, Szymon Gdansk Univ Technol Dept Automat Control Fac Elect Telecommun & Comp Sci Gdansk Poland

In this technical note, we consider the problem of finite-interval parameter smoothing for a class of nonstationary linear stochastic systems subject to both smooth and abrupt parameter changes. The proposed parallel estimation scheme combines the estimates yielded by several exponentially weighted basis function algorithms. The resulting smoother automatically adjusts its smoothing bandwidth to the type and rate of non-stationarity of the identified system. It also allows one to account for the distribution of the measurement noise.

关键词： Identification of nonstationary systems parameter smoothing

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Locally-adaptive Kalman smoothing approach to identification of nonstationary stochastic systems *

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IFAC Proceedings Volumes 2012年第16期45卷 912-917页

作者： Maciej Niedźwiecki Szymon Gackowski Faculty of Electronics Telecommunications and Computer Science Department of Automatic Control Gdańsk University of Technology ul. Narutowicza 11/12 Gdańsk Poland

The problem of noncausal identification of nonstationary, linear stochastic systems, i.e., identification based on prerecorded input/output data, is considered. It is shown how several competing Kalman-type parameter smoothers, differing in smoothness constraints and memory settings, can be combined together to yield a better and more reliable smoothing algorithm. The resulting parallel estimation scheme automatically adjusts its smoothing bandwidth to the unknown, and possibly time-varying, rate of nonstationarity of the identified system. It also allows one to account for parameter jumps and for the distribution of measurement noise.

关键词： system identification nonstationary processes parameter smoothing

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Constrained domain maximum likelihood estimation for naive Bayes text classification

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PATTERN ANALYSIS AND APPLICATIONS 2010年第2期13卷 189-196页

作者： Andres-Ferrer, Jesus Juan, Alfons Univ Politecn Valencia DSIC ITI E-46071 Valencia Spain

The naive Bayes assumption in text classification has the advantage of greatly simplifying maximum likelihood estimation of unknown class-conditional word occurrence probabilities. However, these estimates are usually modified by application of a heuristic parameter smoothing technique to avoid (over-fitted) null estimates. In this work, we advocate the reduction of the parameter domain instead of parameter smoothing. This leads to a constrained domain maximum likelihood estimation problem for which we provide an iterative algorithm that solves it optimally.

关键词： Maximum likelihood estimation Naive Bayes Text classification parameter smoothing Karush-Kuhn-Tucker conditions

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Handwritten Farsi (Arabic) word recognition: a holistic approach using discrete HMM

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PATTERN RECOGNITION 2001年第5期34卷 1057-1065页

作者： Dehghan, M Faez, K Ahmadi, M Shridhar, M Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Amirkabir Univ Technol Dept Elect Engn Tehran Iran Univ Michigan Dept Elect & Comp Engn Dearborn MI 48128 USA

A holistic system for the recognition of handwritten Farsi/Arabic words using right-left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantizatiun is presented. The histogram of chain-code directions of the image strips, scanned from right to left by a sliding window, is used as feature vectors. The neighborhood information preserved in the self-organizing feature map (SOFM), is used for smoothing the observation probability distributions of trained HMMs. Experiments carried out on test samples show promising performance results. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： handwritten word recognition hidden Markov model self-organizing feature map parameter smoothing Farsi/Arabic handwriting recognition

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A hybrid approach to Chinese-English machine translation

A hybrid approach to Chinese-English machine translation

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IEEE International Conference on Intelligent Processing Systems

作者： Liu, Y Liu, Q Zhang, X Chang, BB Chinese Acad Sci Comp Technol Inst Beijing 100080 Peoples R China

ISBN: (纸本)0780342534

A hybrid method to Chinese-English machine translation is presented, a rule-based analysis is combined with statistical data. The rule-based lexical analyzer and syntactic analyzer leave some amount of ambiguity that are resolved using statistical approach. Hidden Markov Model(HMM) is used to return a score for each parts of speech, improved probabilistic context free grammar(PCFG) is used for syntactic scoring, and probabilistic score function is used for semantic aspect. In addition, parameters are usually estimated poorly when the training data is sparse. smoothing the parameters is thus important in the estimation process, back-off procedure is used to smooth the parameters.

关键词： language translation knowledge based systems statistical analysis hidden Markov models context-free grammars probability parameter estimation computational linguistics natural languages hybrid approach Chinese-English machine translation rule-based analysis statistical data rule-based lexical analyzer syntactic analyzer statistical approach hidden Markov model probabilistic context free grammar syntactic scoring probabilistic score function parameter estimation training data parameter smoothing

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Locally parametric nonparametric density estimation

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ANNALS OF STATISTICS 1996年第4期24卷 1619-1647页

作者： Hjort, NL Jones, MC OPEN UNIV DEPT STATMILTON KEYNES MK7 6AABUCKSENGLAND

This paper develops a nonparametric density estimator with parametric overtones. Suppose f(x, theta) is some family of densities, indexed by a vector of parameters theta. We define a local kernel-smoothed likelihood function which, for each x, can be used to estimate the best local parametric approximant to the true density. This leads to a new density estimator of the form f(x, <(theta)over cap (x)>), thus inserting the best local parameter estimate for each new value of x. When the bandwidth used is large, this amounts to ordinary full likelihood parametric density estimation, while for moderate and small bandwidths the method is essentially nonparametric, using only local properties of data and the model. Alternative ways more general than via the local likelihood are also described. The methods can be seen as ways of nonparametrically smoothing the parameter within a parametric class. Properties of this new semiparametric estimator are investigated. Our preferred Version has approximately the same variance as the ordinary kernel method but potentially a smaller bias. The new method is seen to perform better than the traditional kernel method in a broad nonparametric vicinity of the parametric model employed, while at the same time being capable of not losing much in precision to full likelihood methods when the model is correct. Other versions of the method are approximately equivalent to using particular higher order kernels in a semiparametric framework. The methodology we develop can be seen as the density estimation parallel to local likelihood and local weighted least squares theory in nonparametric regression.

关键词： bias reduction density estimation kernel smoothing local likelihood local modelling parameter smoothing semiparametric estimation

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USE OF KOHONEN SELF-ORGANIZING FEATURE MAPS FOR HMM parameter smoothing IN SPEECH RECOGNITION

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IEE PROCEEDINGS-F RADAR AND SIGNAL PROCESSING 1992年第6期139卷 385-390页

作者： ZHAO, Z ROWDEN, CG UNIV ESSEX DEPT ELECTR SYST ENGNCOLCHESTER CO4 3SQESSEXENGLAND

The paper presents a new method for smoothing the parameters of hidden Markov models (HMMs) which produces improved speech recognition results when only a limited amount of training data is available. The method uses the Kohonen self-organising feature map (KSOFM) as a clustering technique in codebook design for discrete HMMs. Neighbouring information provided by a two-dimensional or three-dimensional KSOFM is used to smooth HMM symbol emission probabilities. Tested on a multispeaker isolated digit recognition task, the proposed smoothing method is shown to be robust. Speech recognition results are compared with those obtained by floor smoothing, Parzen smoothing and fuzzy vector quantisation. The major advantage of using a KSOFM as the vector quantiser in a HMM-based speech recogniser is that computational cost in both training and recognition stages can be reduced by using two speedup methods proposed in the paper.

关键词： SPEECH RECOGNITION KOHONEN SELF-ORGANIZING FEATURE MAPS HIDDEN MARKOV MODEL parameter smoothing

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BAYESIAN LEARNING FOR HIDDEN MARKOV MODEL WITH GAUSSIAN MIXTURE STATE OBSERVATION DENSITIES

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SPEECH COMMUNICATION 1992年第2-3期11卷 205-213页

作者： GAUVAIN, JL LEE, CH AT&T BELL LABS SPEECH RES DEPT MURRAY HILL NJ 07974 USA

An investigation into the use of Bayesian learning of the parameters of a multivariate Gaussian mixture density has been carried out. In a framework of continuous density hidden Markov model (CDHMM), Bayesian learning serves as a unified approach for parameter smoothing, speaker adaptation, speaker clustering and corrective training. The goal is to enhance model robustness in a CDHMM-based speech recognition system so as to improve performance. Our approach is to use Bayesian learning to incorporate prior knowledge into the training process in the form of prior densities of the HMM parameters. The theoretical basis for this procedure is presented and results applying it to parameter smoothing, speaker adaptation, speaker clustering and corrective training are given.

关键词： BAYESIAN LEARNING HIDDEN MARKOV MODELS parameter smoothing SPEAKER ADAPTATION SPEAKER CLUSTERING CORRECTIVE TRAINING

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