scholarly journals A Computational Study Assessing Maximum Likelihood and Noniterative Methods for Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models

2012 ◽  
Vol 2012 ◽  
pp. 1-8
Author(s):  
Eric J. Beh ◽  
Thomas B. Farver
Keyword(s):  
Maximum Likelihood ◽  
Newton’S Method ◽  
Newton's Method ◽  
Linear Models ◽  
Computational Study ◽  
Linear Measure ◽  
Iterative Procedures ◽  
Measure Of Association ◽  
Log Linear ◽  
The Impact

For ordinal log-linear models, the estimation of the parameter reflecting the linear-by-linear measure of association has long been a topic for the analysis of dependence for contingency tables. Typically, iterative procedures (including Newton’s method) are used to determine the maximum likelihood estimate of the parameter. Recently Beh and Farver (2009, ANZJS, 51, 335–352) show by way of example three reliable and accurate noniterative techniques that can be used to estimate the parameter and extended this study by examining their reliability computationally. This paper further investigates the reliability of the non-iterative procedures when compared with Newton’s method for estimating this association parameter and considers the impact of the sample size on the estimate.

scholarly journals On Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models: A Computational Study

2012 ◽  
Vol 2012 ◽  
pp. 1-12
Author(s):  
Eric J. Beh ◽  
Thomas B. Farver
Keyword(s):  
Linear Models ◽  
Computational Study ◽  
Contingency Tables ◽  
Linear Parameter ◽  
Iterative Techniques ◽  
Ordinal Variables ◽  
Linear Association ◽  
Iterative Procedures ◽  
Log Linear

Estimating linear-by-linear association has long been an important topic in the analysis of contingency tables. For ordinal variables, log-linear models may be used to detect the strength and magnitude of the association between such variables, and iterative procedures are traditionally used. Recently, studies have shown, by way of example, three non-iterative techniques can be used to quickly and accurately estimate the parameter. This paper provides a computational study of these procedures, and the results show that they are extremely accurate when compared with estimates obtained using Newton’s unidimensional method.

Extracting paraphrase patterns from bilingual parallel corpora

2009 ◽  
Vol 15 (4) ◽  
pp. 503-526 ◽  
Author(s):  
SHIQI ZHAO ◽  
HAIFENG WANG ◽  
TING LIU ◽  
SHENG LI
Keyword(s):  
Natural Language Processing ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Language Processing ◽  
Linear Models ◽  
Likelihood Estimation ◽  
Word Alignment ◽  
Inference Rules ◽  
Parallel Corpora ◽  
Log Linear

AbstractParaphrase patterns are semantically equivalent patterns, which are useful in both paraphrase recognition and generation. This paper presents a pivot approach for extracting paraphrase patterns from bilingual parallel corpora, whereby the paraphrase patterns in English are extracted using the patterns in another language as pivots. We make use of log-linear models for computing the paraphrase likelihood between pattern pairs and exploit feature functions based on maximum likelihood estimation (MLE), lexical weighting (LW), and monolingual word alignment (MWA). Using the presented method, we extract more than 1 million pairs of paraphrase patterns from about 2 million pairs of bilingual parallel sentences. The precision of the extracted paraphrase patterns is above 78%. Experimental results show that the presented method significantly outperforms a well-known method called discovery of inference rules from text (DIRT). Additionally, the log-linear model with the proposed feature functions are effective. The extracted paraphrase patterns are fully analyzed. Especially, we found that the extracted paraphrase patterns can be classified into five types, which are useful in multiple natural language processing (NLP) applications.

The impact of corruption on sport demand

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carlo Amenta ◽  
Paolo Di Betta
Keyword(s):  
Design Methodology ◽  
Linear Models ◽  
Negative Externalities ◽  
Data Set ◽  
Content Type ◽  
Long Run ◽  
Log Linear ◽  
Systemic Corruption ◽  
The Impact

PurposeThe article presents an empirical analysis that evaluates the effects of a systemic corruption scandal on the demand in the short and the long run. In 2006, the Calciopoli scandal uncovered the match rigging in the Italian soccer first division. The exemplary sportive sanction of relegating the primary culprit to the second division imposed further negative externalities on the other clubs. Should we prefer the sportive sanction on the team or the monetary fines for the club?Design/methodology/approachWe estimated two log-linear models of the demand side (stadium attendance) using a fixed effect estimator, on two panel data set made of all the Italian soccer clubs in the first and second division (Serie A and Serie B) for the seasons 2004/2005 to 2009/2010, considering the relegation of the Juventus as the event which impacted the demand for soccer.FindingsRelegating Juventus to Serie B caused an immediate decrease of 18.4% in the attendance for all the teams, both in Serie A and in Serie B, for the three seasons considered, and 1% decrease when all the seasons are considered to measure the fallout of the scandal on the fans' disaffection.Originality/valueThe effect of corruption in sport on demand is an important issue, and there are few studies already published. As for sports economics and management, our results are of interest for sport-governing bodies – as a case study that can help in designing a more effective sanctioning system to prevent corruption episodes.

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