Changes in the distribution of Poplar Box (Eucalyptus populnea) on major soil groups: An application of the log-linear model.

1981 ◽  
Vol 3 (1) ◽  
pp. 33 ◽  
Author(s):  
RB Cunningham ◽  
AA Webb ◽  
A Mortlock
Keyword(s):  
Statistical Analysis ◽  
Linear Model ◽  
Generalized Linear Models ◽  
Gradient Analysis ◽  
Linear Models ◽  
Geographic Location ◽  
Significance Tests ◽  
Log Linear ◽  
Eucalyptus Populnea ◽  
Soil Groups

The association of poplar box (Eucalyptus populnea) with five main soil groups is examined. A statistical analysis, using a log- linear model, indicated that the relative frequencies of poplar box sites occumng on major soil groups changed with geographic location. The change in distribution is shown to relate to climate, as indicated by summer and winter moisture indices and the diff- erence between them. This study illustrates the use of log-linear models in ecology; such models, and more generally, Generalized Linear Models, in providing significance tests, have advantages over the non-statistical methods of gradient analysis.

scholarly journals A Cascaded Unsupervised Model for PoS Tagging

2021 ◽  
Vol 20 (1) ◽  
pp. 1-23
Author(s):  
Necva Bölücü ◽  
Burcu Can
Keyword(s):  
Linear Model ◽  
Language Processing ◽  
Bayesian Model ◽  
Linear Models ◽  
Syntactic Category ◽  
Semantic Parsing ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Sentence Level ◽  
Log Linear

Part of speech (PoS) tagging is one of the fundamental syntactic tasks in Natural Language Processing, as it assigns a syntactic category to each word within a given sentence or context (such as noun, verb, adjective, etc.). Those syntactic categories could be used to further analyze the sentence-level syntax (e.g., dependency parsing) and thereby extract the meaning of the sentence (e.g., semantic parsing). Various methods have been proposed for learning PoS tags in an unsupervised setting without using any annotated corpora. One of the widely used methods for the tagging problem is log-linear models. Initialization of the parameters in a log-linear model is very crucial for the inference. Different initialization techniques have been used so far. In this work, we present a log-linear model for PoS tagging that uses another fully unsupervised Bayesian model to initialize the parameters of the model in a cascaded framework. Therefore, we transfer some knowledge between two different unsupervised models to leverage the PoS tagging results, where a log-linear model benefits from a Bayesian model’s expertise. We present results for Turkish as a morphologically rich language and for English as a comparably morphologically poor language in a fully unsupervised framework. The results show that our framework outperforms other unsupervised models proposed for PoS tagging.

scholarly journals The Application of Log-Linear Model to Selected Poison Patients

2020 ◽  
pp. 1-7
Author(s):  
Fatin N.S.A. ◽  
Norlida M.N. ◽  
Siti Z.M.J.
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Demographic Data ◽  
Contingency Tables ◽  
Categorical Variables ◽  
State Variables ◽  
Exposure Route ◽  
Higher Dimensional ◽  
Route Of Exposure ◽  
Log Linear

Log-linear model is a technique used to analyze the cross-classification categorical data or the contingency table. It is used to obtain the parsimony models that describe the interaction between the categorical variables in contingency tables. Log-linear models are commonly used in evaluating higher dimensional contingency tables that involves more than two categorical variables. This study focuses on analyzing data of poisoned patients from 2012 to 2014 using log-linear model. There are two model analyzed; model for demographic data of patients and model of poisoning information. For the first model, the variables involved are gender, age, race and state. Variables for the second model are circumstance of exposure, type of exposure, location of exposure, route of exposure and types of poison. Both log-linear models are developed to investigate the association between variables in the model. As a result of this study, the best model for demographic data and poisoning information are the model with three-ways interaction. For the best model of demographic data, there is an association between gender, age and race, race, gender and state as well as age, race and state. Meanwhile, the best model for poisoning information reveals that there is relationship between circumstance of exposure, route of exposure and type of poison, location of exposure, route of exposure and type of poison, circumstance of exposure, type of exposure and route of exposure, circumstance of exposure, location of exposure and route of exposure, circumstance of exposure, type of exposure and type of poison and also type of exposure, location of exposure and type of poison. Keywords: log-linear; demographic; gender; age; race; state; circumstance of exposure; type of exposure; location of exposure; route of exposure; types of poison

Tics and Heredity

1987 ◽  
Vol 150 (5) ◽  
pp. 628-634 ◽  
Author(s):  
D. M. Zausmer ◽  
M. E. Dewey
Keyword(s):  
Statistical Analysis ◽  
Linear Models ◽  
Statistical Analyses ◽  
Tourette's Syndrome ◽  
Reasonable Doubt ◽  
Tourette’S Syndrome ◽  
Genetic Transmission ◽  
Explanatory Variables ◽  
Simple Mode ◽  
Log Linear

The limited literature on the pedigrees of tiqueurs, including those with Gilles de la Tourette's syndrome, is reviewed. Most statistical analyses have been restricted to affected family members without specifying the unaffected ones. The present statistical analysis of a series of child tiqueurs, including 91 probands and 1293 first- and second-degree relatives, 46 of whom were tiqueurs, predicts the odds on being a tiqueur for individuals, and establishes how those odds are affected by certain explanatory variables using log-linear models. The data do not confirm a familial pattern beyond reasonable doubt, but if the suggested prevalence of tics in the population is 10% then the figure for parents is large enough to support a familial hypothesis. The pedigrees do not indicate a simple mode of genetic transmission. Further research is needed to confirm that there is a connection between childhood tics and Gilles de la Tourette's syndrome, to establish that the predisposition to tics is familial, and, if so, whether there is a complex genetic mechanism involved, or some other environmental aetiology so far undisclosed.

scholarly journals Market model analysis and forecasting behavior of watermelon production in Bangladesh

2014 ◽  
Vol 26 (1-2) ◽  
pp. 47-56
Author(s):  
Murshida Khanam ◽  
Umme Hafsa
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Linear Models ◽  
Time Series Data ◽  
Arima Model ◽  
Model Analysis ◽  
Market Model ◽  
Series Data ◽  
Log Linear

An attempt has been made to study various models regarding watermelon production in Bangladesh and to identify the best model that may be used for forecasting purposes. Here, supply, log linear, ARIMA, MARMA models have been used to do a statistical analysis and forecasting behavior of production of watermelon in Bangladesh by using time series data covering whole Bangladesh. It has been found that, between the supply and log linear models; log linear is the best model. Comparing ARIMA and MARMA models it has been concluded that ARIMA model is the best for forecasting purposes. DOI: http://dx.doi.org/10.3329/bjsr.v26i1-2.20230 Bangladesh J. Sci. Res. 26(1-2): 47-56, December-2013

Étude du biais dans le modèle log-linéaire d'estimation régionale

2004 ◽  
Vol 31 (2) ◽  
pp. 361-368 ◽  
Author(s):  
Claude Girard ◽  
Taha B.M.J Ouarda ◽  
Bernard Bobée
Keyword(s):  
Linear Relationship ◽  
Key Words ◽  
Linear Model ◽  
Linear Models ◽  
Target Variable ◽  
Independent Variables ◽  
Regional Estimation ◽  
Log Linear

Log-linear models are frequently used in hydrology, especially for the regional estimation of flood volumes based on the physiographic data of a set of basins. A log-linear model describes a linear relationship between the log of a dependant variable and independent variables which are functions of parameters, of which the value remains to be determined. It is determined by using a set of basins with known values of dependant and independent variables. The model is then used to obtain a prediction for the dependant variable logarithm of a basin of interest, based on the known values of independent variables in the model. This prediction is unbiased with relation to the log of the target variable. However, the exponential value of this prediction is biased with relation to the target variable. This paper addresses the measures to correct the bias in the prediction, which is introduced by exponentiation; the impacts on the variance of the ensuing predictions is also discussed. Key words: bias, transformation, log-linear model.[Journal translation]

Latency models: the statistical analysis of response times

1982 ◽  
Vol 12 (3) ◽  
pp. 659-665 ◽  
Author(s):  
Graham Dunn ◽  
Din Master
Keyword(s):  
Statistical Analysis ◽  
Statistical Methods ◽  
Linear Models ◽  
Response Times ◽  
Descriptive Statistics ◽  
Failure Times ◽  
Log Linear

SYNOPSISThis paper introduces statistical methods suitable for the analysis of response, survival or failure times and, in particular, latencies measured in experiments on the speed of recall of memories. The discussion includes the use of simple descriptive statistics, as well as an explanation of the role of linear-logistic and log-linear models.

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