Results 1  10
of
114
Geoadditive Models
, 2000
"... this paper is a recent article on modelbased geostatistics by Diggle, Tawn and Moyeed (1998) where pure kriging (i.e. no covariates) is the focus. Our paper inherits some of its aspects: modelbased and with mixed model connections. In particular the comment by Bowman (1998) in the ensuing discussi ..."
Abstract

Cited by 79 (3 self)
 Add to MetaCart
this paper is a recent article on modelbased geostatistics by Diggle, Tawn and Moyeed (1998) where pure kriging (i.e. no covariates) is the focus. Our paper inherits some of its aspects: modelbased and with mixed model connections. In particular the comment by Bowman (1998) in the ensuing discussion suggested that additive modelling would be a worthwhile extension. This paper essentially follows this suggestion. However, this paper is not the first to combine the notions of geostatistics and additive modelling. References known to us are Kelsall and Diggle (1998), Durban Reguera (1998) and Durban, Hackett, Currie and Newton (2000). Nevertheless, we believe that our approach has a number of attractive features (see (1)(4) above), not all shared by these references. Section 2 describes the motivating application and data in detail. Section 3 shows how one can express additive models as a mixed model, while Section 4 does the same for kriging and merges the two into the geoadditive model. Issues concerning the amount of smoothing are discussed in Section 5 and inferential aspects are treated in Section 6. Our analysis of the Upper Cape Cod reproductive data is presented in Section 7. Section 8 discusses extension to the generalised context.We close the paper with some disussion in Section 9. 2 Description of the application and data
Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime
 ANN. STATIST
, 2004
"... An autoregressive process with Markov regime is an autoregressive process for which the regression function at each time point is given by a nonobservable Markov chain. In this paper we consider the asymptotic properties of the maximum likelihood estimator in a possibly nonstationary process of this ..."
Abstract

Cited by 64 (8 self)
 Add to MetaCart
An autoregressive process with Markov regime is an autoregressive process for which the regression function at each time point is given by a nonobservable Markov chain. In this paper we consider the asymptotic properties of the maximum likelihood estimator in a possibly nonstationary process of this kind for which the hidden state space is compact but not necessarily finite. Consistency and asymptotic normality are shown to follow from uniform exponential forgetting of the initial distribution for the hidden Markov chain conditional on the observations.
flda: matrix factorization through latent dirichlet allocation
 In Proc. of WSDM ’10
, 2010
"... We propose fLDA, a novel matrix factorization method to predict ratings in recommender system applications where a “bagofwords ” representation for item metadata is natural. Such scenarios are commonplace in web applications like content recommendation, ad targeting and web search where items ar ..."
Abstract

Cited by 43 (0 self)
 Add to MetaCart
(Show Context)
We propose fLDA, a novel matrix factorization method to predict ratings in recommender system applications where a “bagofwords ” representation for item metadata is natural. Such scenarios are commonplace in web applications like content recommendation, ad targeting and web search where items are articles, ads and web pages respectively. Because of data sparseness, regularization is key to good predictive accuracy. Our method works by regularizing both user and item factors simultaneously through user features and the bag of words associated with each item. Specifically, each word in an item is associated with a discrete latent factor often referred to as the topic of the word; item topics are obtained by averaging topics across all words in an item. Then, user rating on an item is modeled as user’s affinity to the item’s topics where user affinity to topics (user factors) and topic assignments to words in items (item factors) are learned jointly in a supervised fashion. To avoid overfitting, user and item factors are regularized through Gaussian linear regression and Latent Dirichlet Allocation (LDA) priors respectively. We show our model is accurate, interpretable and handles both coldstart and warmstart scenarios seamlessly through a single model. The efficacy of our method is illustrated on benchmark datasets and a new dataset from Yahoo! Buzz where fLDA provides superior predictive accuracy in coldstart scenarios and is comparable to stateoftheart methods in warmstart scenarios. As a byproduct, fLDA also identifies interesting topics that explains useritem interactions. Our method also generalizes a recently proposed technique called supervised LDA (sLDA) to collaborative filtering applications. While sLDA estimates item topic vectors in a supervised fashion for a single regression, fLDA incorporates multiple regressions (one for each user) in estimating the item factors.
Random Utility Theory for Social Choice
"... Random utility theory models an agent’s preferences on alternatives by drawing a realvalued score on each alternative (typically independently) from a parameterized distribution, and then ranking the alternatives according to scores. A special case that has received significant attention is the Pla ..."
Abstract

Cited by 21 (10 self)
 Add to MetaCart
(Show Context)
Random utility theory models an agent’s preferences on alternatives by drawing a realvalued score on each alternative (typically independently) from a parameterized distribution, and then ranking the alternatives according to scores. A special case that has received significant attention is the PlackettLuce model, for which fast inference methods for maximum likelihood estimators are available. This paper develops conditions on general random utility models that enable fast inference within a Bayesian framework through MCEM, providing concave loglikelihood functions and bounded sets of global maxima solutions. Results on both realworld and simulated data provide support for the scalability of the approach and capability for model selection among general random utility models including PlackettLuce. 1
A survey of Monte Carlo algorithms for maximizing the likelihood of a twostage hierarchical model
, 2001
"... Likelihood inference with hierarchical models is often complicated by the fact that the likelihood function involves intractable integrals. Numerical integration (e.g. quadrature) is an option if the dimension of the integral is low but quickly becomes unreliable as the dimension grows. An alternati ..."
Abstract

Cited by 19 (9 self)
 Add to MetaCart
Likelihood inference with hierarchical models is often complicated by the fact that the likelihood function involves intractable integrals. Numerical integration (e.g. quadrature) is an option if the dimension of the integral is low but quickly becomes unreliable as the dimension grows. An alternative approach is to approximate the intractable integrals using Monte Carlo averages. Several dierent algorithms based on this idea have been proposed. In this paper we discuss the relative merits of simulated maximum likelihood, Monte Carlo EM, Monte Carlo NewtonRaphson and stochastic approximation. Key words and phrases : Eciency, Monte Carlo EM, Monte Carlo NewtonRaphson, Rate of convergence, Simulated maximum likelihood, Stochastic approximation All three authors partially supported by NSF Grant DMS0072827. 1 1
The EM algorithm
 Wiley Series in Probability and Statistics: Applied Probability and Statistics, WileyInterscience
, 1997
"... Die ZBW räumt Ihnen als Nutzerin/Nutzer das unentgeltliche, räumlich unbeschränkte und zeitlich auf die Dauer des Schutzrechts beschränkte einfache Recht ein, das ausgewählte Werk im Rahmen der unter ..."
Abstract

Cited by 18 (2 self)
 Add to MetaCart
(Show Context)
Die ZBW räumt Ihnen als Nutzerin/Nutzer das unentgeltliche, räumlich unbeschränkte und zeitlich auf die Dauer des Schutzrechts beschränkte einfache Recht ein, das ausgewählte Werk im Rahmen der unter
Simple Incorporation of Interactions Into Additive Models
, 2000
"... This article presents penalized spline models that incorporate factor by curve interactions into additive models. A mixed model formulation for penalized splines allows for straightforward model fitting, smoothing parameter selection, and hypothesis testing. We illustrate the proposed model by apply ..."
Abstract

Cited by 16 (2 self)
 Add to MetaCart
This article presents penalized spline models that incorporate factor by curve interactions into additive models. A mixed model formulation for penalized splines allows for straightforward model fitting, smoothing parameter selection, and hypothesis testing. We illustrate the proposed model by applying it to pollen ragweed data in which seasonal trends vary by year.
User reputation in a comment rating environment
 In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’11
, 2011
"... Reputable users are valuable assets of a web site. We focus on user reputation in a comment rating environment, where users make comments about content items and rate the comments of one another. Intuitively, a reputable user posts high quality comments and is highly rated by the user community. To ..."
Abstract

Cited by 14 (2 self)
 Add to MetaCart
(Show Context)
Reputable users are valuable assets of a web site. We focus on user reputation in a comment rating environment, where users make comments about content items and rate the comments of one another. Intuitively, a reputable user posts high quality comments and is highly rated by the user community. To our surprise, we find that the quality of a comment judged editorially is almost uncorrelated with the ratings that it receives, but can be predicted using standard text features, achieving accuracy as high as the agreement between two editors! However, extracting a pure reputation signal from ratings is difficult because of data sparseness and several confounding factors in users ’ voting behavior. To address these issues, we propose a novel biassmoothed tensor model and empirically show that our model significantly outperforms a number of alternatives based on Yahoo! News, Yahoo! Buzz and Epinions datasets.
Implementing and Diagnosing the Stochastic Approximation EM Algorithm
 Journal of Computational and Graphical Statistics
, 2006
"... The stochastic approximation EM (SAEM) algorithm is a simulationbased alternative to the EM (Expectation/Maximization) algorithm for situations when the Estep is hard or impossible. One of the appeals of SAEM is that, unlike other Monte Carlo versions of EM, it converges with a fixed (and typicall ..."
Abstract

Cited by 13 (2 self)
 Add to MetaCart
(Show Context)
The stochastic approximation EM (SAEM) algorithm is a simulationbased alternative to the EM (Expectation/Maximization) algorithm for situations when the Estep is hard or impossible. One of the appeals of SAEM is that, unlike other Monte Carlo versions of EM, it converges with a fixed (and typically small) simulation size. Another appeal is that, in practice, the only decision that has to be made is the choice of the step size which is a onetime decision and which is usually done before starting the method. The downside of SAEM is that there exist no datadriven and/or modeldriven recommendations as to the magnitude of this step size. We argue in this paper that a challenging model/data combination coupled with an unlucky step size can lead to very poor algorithmic performance and, in particular, to a premature stop of the method. We propose a new heuristic for SAEM’s step size selection based on the underlying EM rate of convergence. We also use the muchappreciated EM likelihoodascent property to derive a new and flexible way of monitoring the progress of the SAEM algorithm. We apply the method to a challenging geostatistical model of online retailing.