Generalized Log-Linear Models of Housing Choice

1988 ◽  
Vol 20 (1) ◽  
pp. 55-69 ◽  
Author(s):  
M C Deurloo ◽  
F M Dieleman ◽  
W A V Clark
Keyword(s):  
Linear Models ◽  
Nonlinear Effects ◽  
Design Matrix ◽  
Housing Choice ◽  
Ordered Categories ◽  
Public And Private ◽  
Income Effects ◽  
Additional Information ◽  
Log Linear

By incorporating the structure of polytomous variables with ordered categories in the design matrix, nonstandard logit models are used to analyze housing choice. The detailed effects of income, age, and type of housing market on choice are examined. The additional information that is incorporated in the modeling leads to a more parsimonious representation of the data. The results confirm the central and substantial role of income; income effects are linear for owners but there are nonlinear effects for public and private renters. There are important age and region interaction effects on choice for households originally in the rental sector, and for former owners the value of the previous dwelling influences choice.

scholarly journals Role of Remittance in Economic Development of Nepal

2008 ◽  
Vol 4 (1) ◽  
pp. 28-37 ◽  
Author(s):  
Nagendra Lal Srivastava ◽  
Shashi K. Chaudhary
Keyword(s):  
Growth Rates ◽  
Linear Models ◽  
Positive Impact ◽  
Real Term ◽  
Development Indicators ◽  
Business Studies ◽  
Dependent Variables ◽  
Log Linear ◽  
The Impact

This article deals with the analysis of direct impact of remittance on three development indicators viz. GDP, GNP and PCI of the nation which are also the dependent variables of the proposed models. The analysis has been carried out with linear and log-linear models under multiple regressions. The impact of remittance has been seen most remarkable in the GDP and GNP both in nominal and real terms. In the nominal GDP and GNP, the remittance shows 61 percent and 72 percent impact respectively while in real term it shows 48 percent and 55 percent respectively. It has also shown positive impact on the PCI but it is comparatively low (four percent in nominal and one percent in real terms). The growth rates of independent variables (Rm, K, L and X) have also been tested in the same model to find the effects on the dependent variables. The findings are positive except for labor force, but they are marginal which show that remittance has not been used effectively so as to increase the real growth rates of the economy. The Journal of Nepalese Business Studies Vol. IV, No.1 (2007) pp. 28-37

scholarly journals Modelling Tabular Data with an Ordered Outcome

1996 ◽  
Vol 1 (3) ◽  
pp. 1-10
Author(s):  
Vernon Gayle
Keyword(s):  
Model Building ◽  
Linear Models ◽  
Statistical Modelling ◽  
Categorical Variables ◽  
Sociological Research ◽  
Ratio Model ◽  
Ordered Categories ◽  
Continuation Ratio ◽  
Recent Developments ◽  
Log Linear

A large amount of data that is considered within sociological studies consists of categorical variables that lend themselves to tabular analysis. In the sociological analysis of data regarding social class and educational attainment, for example, the variables of interest can often plausibly be considered as having a substantively interesting order. Standard log-linear models do not take ordinality into account, thereby potentially they may disregard useful information. Analyzing tables where the response variable has ordered categories through model building has been problematic in software packages such as GLIM (Aitken et al., 1989). Recent developments in statistical modelling have offered new possibilities and this paper explores one option, namely the continuation ratio model which was initially reported by Fienberg and Mason (1979). The fitting of this model to data in tabular form is possible in GLIM although not especially trivial and by and large this approach has not been employed in sociological research. In this paper I outline the continuation ratio model and comment upon how it can be fitted to data by sociologists using the GLIM software. In addition I present a short description of the relative merits of such an approach. Presenting this paper in an electronic format facilitates the possibility of replicating the analysis. The data is appended to the paper in the appropriate format along with a copy of the GLIM transcript. A dumped GLIM4 file is also attached.

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.

scholarly journals Exploratory analysis and error modeling of a sequencing technology

2016 ◽  
Author(s):  
Michael Inouye ◽  
Kerrin S Small ◽  
Yik Y Teo ◽  
Heng Li ◽  
Nava Whiteford ◽  
...  
Keyword(s):  
Linear Models ◽  
Sequence Data ◽  
Artificial Chromosome ◽  
Error Modeling ◽  
Illumina Genome Analyzer ◽  
Data Generation ◽  
Sequencing Technology ◽  
Additional Information ◽  
Base Position ◽  
Log Linear

Next generation DNA sequencing methods have created an unprecedented leap in sequence data generation, thus novel computational tools and statistical models are required to optimize and assess the resulting data. In this report, we explore underlying causes of error for the Illumina Genome Analyzer (IGA) sequencing technology and attempt to quantify their effects using a human bacterial artificial chromosome sequenced to 60,000 fold coverage. Seven potential error predictors are considered: Phred score, read entropy, tile coordinates, local tile density, base position within read, nucleotide call, and lane. With these parameters, logistic regression and log-linear models are constructed and used to show that each of the potential predictors contributes to error (P<1x10-4). With this additional information, we apply the logistic model and achieve a 3% improvement in both the sensitivity and specificity to detect IGA errors. Further, we demonstrate that these modeling approaches can be used as a feedback loop to inform laboratory methods and identify specific machine or run bias.

scholarly journals A Framework to Interpret Nonstandard Log-Linear Models

2016 ◽  
Vol 36 (2) ◽  
Author(s):  
Patrick Mair
Keyword(s):  
Generalized Linear Models ◽  
Categorical Data ◽  
Linear Models ◽  
Contingency Tables ◽  
Design Matrix ◽  
Nested Models ◽  
Testing Strategies ◽  
The Family ◽  
Parsimonious Models ◽  
Log Linear

The formulation of log-linear models within the framework of Generalized Linear Models offers new possibilities in modeling categorical data. The resulting models are not restricted to the analysis of contingency tables in terms of ordinary hierarchical interactions. Such models are considered as the family of nonstandard log-linear models. The problem that can arise is an ambiguous interpretation of parameters. In the current paperthis problem is solved by looking at the effects coded in the design matrix and determining the numerical contribution of single effects. Based on these results, stepwise approaches are proposed in order to achieve parsimonious models. In addition, some testing strategies are presented to test such (eventually non-nested) models against each other. As a result, a whole interpretation framework is elaborated to examine nonstandard log-linear models in depth.

The Changing Role of Education in the Marriage Market:Assortative Marriage in Canada and the United States since the 1970s

2008 ◽  
Vol 33 (2) ◽  
Author(s):  
Feng Hou ◽  
John Myles
Keyword(s):  
United States ◽  
Young Adults ◽  
Linear Models ◽  
Census Data ◽  
The United States ◽  
Educational Homogamy ◽  
Educational Distribution ◽  
Log Linear ◽  
Changing Role

Whether or not relative rates of assortative marriage have been rising in the affluent democracies has been subject to considerable dispute. First, we show how the conflicting empirical findings that have fueled the debate are frequently an artifact of alternative methodological strategies for answering the question. Then, drawing on comparable census data for Canada and the United States, we examine trends in educational homogamy and intermarriage with log-linear models for all marriages among young adults under 35 over three decades. Our results show that educational homogamy, the tendency of like to marry like, has unambiguously risen in both countries since the 1970s. Rising levels of marital homogamy were the result of declining intermarriage at both ends of the educational distribution.

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