scholarly journals MEDIA MIX MODELING COMPARISON OF INTERACTION MODEL TO SIMPLE LOG-LINEAR MODEL

2017 ◽  
Vol 10 (13) ◽  
pp. 390
Author(s):  
Priyanka Sharma ◽  
M Janaki Meena ◽  
S P Syed Ibrahim
Keyword(s):  
Linear Model ◽  
Linear Method ◽  
Interaction Model ◽  
Combined Effect ◽  
New Model ◽  
Simultaneous Effect ◽  
Log Linear ◽  
Media Mix

The objective of current study is to compare a new model for media mix problem with popular model named as simple log linear model. A modified approach proposed to improve the results of media mix model from simple log linear method includes the simultaneous effect of different media variables on sales. The combined effect caused by various media variables shows a synergy in the curve for sales and hence considering it makes the model much effective and accurate.

scholarly journals Flexible Log-Linear Birnbaum–Saunders Model

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1188
Author(s):  
Guillermo Martínez-Flórez ◽  
Inmaculada Barranco-Chamorro ◽  
Héctor W. Gómez
Keyword(s):  
Normal Distribution ◽  
Linear Model ◽  
General Model ◽  
Lifetime Data ◽  
Skew Normal Distribution ◽  
New Model ◽  
Log Linear ◽  
Skew Normal

Rieck and Nedelman (1991) introduced the sinh-normal distribution. This model was built as a transformation of a N(0,1) distribution. In this paper, a generalization based on a flexible skew normal distribution is introduced. In this way, a more general model is obtained that can describe a range of asymmetric, unimodal and bimodal situations. The paper is divided into two parts. First, the properties of this new model, called flexible sinh-normal distribution, are obtained. In the second part, the flexible sinh-normal distribution is related to flexible Birnbaum–Saunders, introduced by Martínez-Flórez et al. (2019), to propose a log-linear model for lifetime data. Applications to real datasets are included to illustrate our findings.

Polytomous IRT models under log-linear model framework

2011 ◽  
Author(s):  
Zhushan Mandy Li
Keyword(s):  
Linear Model ◽  
Model Framework ◽  
Irt Models ◽  
Polytomous Irt Models ◽  
Log Linear

Leveraging Second-Order Log-Linear Model for Improved Deep Learning Based ASR Performance

2018 ◽  
Author(s):  
Ankit Raj ◽  
Shakti P Rath ◽  
Jithendra Vepa
Keyword(s):  
Deep Learning ◽  
Linear Model ◽  
Second Order ◽  
Log Linear

Asymptotic results for a conditional Poisson log-linear model

1989 ◽  
Vol 7 (4) ◽  
pp. 267-270 ◽  
Author(s):  
Douglas G Bonett ◽  
P.M Bentler ◽  
J.Arthur Woodward
Keyword(s):  
Linear Model ◽  
Asymptotic Results ◽  
Log Linear ◽  
Conditional Poisson

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 Application of log-linear model in inference on karyotypic evolution in chronic myelocytic leukemia.

1990 ◽  
Vol 87 ◽  
pp. 135-141 ◽  
Author(s):  
T Hashimoto ◽  
M Ohtaki ◽  
N Kamada ◽  
H Yamamoto ◽  
M Munaka
Keyword(s):  
Linear Model ◽  
Chronic Myelocytic Leukemia ◽  
Karyotypic Evolution ◽  
Myelocytic Leukemia ◽  
Log Linear

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

EVALUATION OF PROTEIN IN FOODS VIII. INFLUENCE OF QUALITY AND QUANTITY OF DIETARY PROTEIN ON NET PROTEIN UTILIZATION

1963 ◽  
Vol 41 (2) ◽  
pp. 275-281 ◽  
Author(s):  
A. B. Morrison ◽  
Z. I. Sabry ◽  
N. T. Gridgeman ◽  
J. A. Campbell
Keyword(s):  
Linear Model ◽  
Dietary Protein ◽  
Nutritional Value ◽  
Prediction Equation ◽  
Protein Utilization ◽  
New Model ◽  
Chemical Score ◽  
Net Protein ◽  
Maximum Utilization ◽  
General Prediction

The relation between the percentage of dietary Calories derived from protein (P) and the corresponding net protein utilization (N.P.U. or U) was studied with various proteins of different nutritional value. At P > 40 the experimental results (from rats) did not fit the Miller–Payne linear model. A semilogarithmic plot, however, accommodated all the data, ranging from P = 10 to P = 80. The new model yields, by extrapolation, values of maximum utilization that agree well with the protein (chemical) scores. A general prediction equation is given that enables N.P.U. to be calculated from a knowledge of P and of the chemical score of the protein or protein mixture at issue.

scholarly journals Scrapie-resistant sheep show certain coat colour characteristics

2009 ◽  
Vol 91 (1) ◽  
pp. 39-46 ◽  
Author(s):  
R. M. SAWALHA ◽  
L. BELL ◽  
S. BROTHERSTONE ◽  
I. WHITE ◽  
A. J. WILSON ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Linear Model ◽  
Morphological Diversity ◽  
Coat Colour ◽  
Unintended Consequences ◽  
Colour Pattern ◽  
Reverse Pattern ◽  
Colour Patterns ◽  
Log Linear ◽  
The Relationship

SummarySusceptibility to scrapie is known to be associated with polymorphisms at the prion protein (PrP) gene, and this association is the basis of current selective programmes implemented to control scrapie in many countries. However, these programmes might have unintended consequences for other traits that might be associated withPrPgenotype. The objective of this study was to investigate the relationship betweenPrPgenotype and coat colour characteristics in two UK native sheep breeds valued for their distinctive coat colour patterns. Coat colour pattern, darkness and spotting andPrPgenotype records were available for 11 674 Badgerfaced Welsh Mountain and 2338 Shetland sheep. The data were analysed with a log–linear model using maximum likelihood. Results showed a strong significant association ofPrPgenotype with coat colour pattern in Badgerfaced Welsh Mountain and Shetland sheep and with the presence of white spotting in Shetland sheep. Animals with the ARR/ARR genotype (the most scrapie resistant) had higher odds of having a light dorsum and a dark abdomen than the reverse pattern. The implication of these associations is that selection to increase resistance to scrapie based only onPrPgenotype could result in change in morphological diversity and affect other associated traits such as fitness.

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