1
1735-1294
Statistical Research and Training Center - Statistical Centre of Iran
125
General
Sensitivity Analysis of Spatial Sampling Designs for Optimal Prediction
Farzaneh Kharajoo
N.
Jafari Khaledi
M.
1
9
2008
5
1
1
18
20
01
2016
20
01
2016
In spatial statistic, the data analyzed which is correlated and this correlation is due to their locations in the studied region. Such correlation that is related to distance between observations is called spatial correlation. Usually in spatial data analysis, the prediction of the amount of uncertain quantity in arbitrary 4locations of the area is considered according to attained observations from sampling points. Thus, supposing being certain the sample size, it is necessary to select a sampling design which its observations are attained from the best prediction in mentioned points that is called spatial sampling design for prediction. In this paper, the determination of such design is considered.
For this, suppose that ... [To continue please click here]
123
General
Partial Association Components in Multi-way Contingency Tables and Their Statistiical Analysis
Ghoreishi
k.
Meshkani
R.
1
9
2008
5
1
19
32
20
01
2016
20
01
2016
In analyses of contingency tables made up of categorical variables, the study of relationship between the variables is usually the major objective. So far, many association measures and association models have been used to measure the association structure present in the table. Although the association measures merely determine the degree of strength of association between the study variables, the association models illustrate the details and components of association structure. These measure and models have found vast application in many disciplines. For more details see Goodman and Kruskal (1954, 1979), Leibetrau (1983), Goodman (1972, 1979, 1985, 1991) and Ghoreishi and Meshkani (2006, 2008).
When one is interested in demonstrating the association components of two ordinal categorical variables while there is at least one explanatory categorical variable, it is natural to think of partial association between the two ordinal variables while the effect of the explanatory variable(s) are averaged out in some sense. We believe that this approach is the best way to incorporate both the extra information available in explanatory variable(s) into analysis and interpret the role and share of various polynomial trends.
In this paper, we ... [To continue please click here]
122
General
Inference for a Skew Normal Distribution Based on Progressively Type-II Censored Samples
Asgharzadeh
A.
Moradinejad
P.
1
9
2008
5
1
33
56
20
01
2016
20
01
2016
In many industrial experiments involving lifetimes of machines or units, experiments have to be terminated early or the number of experiments must be limited due to a variety of circumstances (e.g. when expensive, etc.) the samples that arise from such experiments are called censored data.
Cohen (1991) was one of the earliest to study a more general censoring scheme called progressive censoring scheme. The progressive Type-II censoring scheme, after starting the life-testing experiment with ....[To continue please click here]
126
General
Methods for Parameter Estimation of the Lorenz Functional Forms and Compare Them Based on Household Expenses Data
Mojiri
A.
Mohtashami Borzadaran
R.
Waghei
Y.
1
9
2008
5
1
57
74
20
01
2016
20
01
2016
In the modern society and specially in our country discussion of poverty, wealth and social justice are the most important arguments of public and private circles. The most important graphical tools which are used to describe the quantity of centralization like wealth in a society is Lorenz curve. In these situations, most of econometricians measure the economic inequalities. In the discrete case, the Lorenz curve is therefore defined as ....[To continue please click here]
127
General
Transition Models for Analyzing Longitudinal Data with Bivariate Mixed Ordinal and Nominal Responses
Rezaei Ghahroodi
Zahra
Ganjali
Mojtaba
Harandi
Fatemeh
1
9
2008
5
1
75
94
20
01
2016
20
01
2016
In many longitudinal studies, nominal and ordinal mixed bivariate responses are measured. In these studies, the aim is to investigate the effects of explanatory variables on these time-related responses. A regression analysis for these types of data must allow for the correlation among responses during the time. To analyze such ordinal-nominal responses, using a proposed weighting approach, an ordinal and nominal mixed transition model is proposed and then maximum likelihood method is used to find the parameter estimates. The likelihood function in this method is partitioned to make possible the use of existing software.
Social-economical and political consequences arising from Iranian unemployment in the community and necessity of familiarity with the labor force characteristics particularly identification of the structure of changes in economic activity status of the Iranian population are important in order to achieve the objectives of social-economic and cultural development plans of the country. Data of Labor Force Survey in Iran are in a longitudinal form... [To continue please click here]
124
General
Functional Modeling of Iranian Precipitation Based on Temperature and Humidity
Hosseini-nasab
E.
Kheirolah-zadeh
N.
Tazikeh Miyandarreh
N.
1
9
2008
5
1
95
121
20
01
2016
20
01
2016
Functional Data Analysis (FDA) has recently made considerable progress because of easier access to the data that are essentially in the form of curves. Modeling of Iranian precipitation based on temperature and humidity with continuous the essential nature of such phenomena that are continuous functions of time has not been done properly. The corresponding data are generally collected daily or monthly (discretely). However, if one treats those data as multivariate observations and analyzes by multivariate methods, it may cause some problems such as infinite number of solutions for normal equations in regression.
In regard to the fact that the original discrete data must be firstly converted to continuous functions, we usually use “basis function methods” for dimension reduction of the data due to its simplicity. In this article, problems arising form using multiple regression methods instead of applying functional regression approaches are discussed.
We have treated a real dataset that was collected from 102 Iranian weather stations in 2006. The dataset ... [To continue please click here]