scholarly journals Nonresponse in stratified sampling: a mathematical programming approach

2011 ◽  
Vol 29 (1) ◽  
pp. 40 ◽  
Author(s):  
Ummatul Fatima ◽  
M. J. Ahsan
Keyword(s):  
Nonlinear Programming ◽  
Mathematical Programming ◽  
Stratified Sampling ◽  
Programming Approach ◽  
Nonlinear Programming Problem ◽  
Randomized Response ◽  
Optimization Software ◽  
Sensitive Question ◽  
Integer Nonlinear Programming ◽  
Mathematical Programming Approach

In sampling theory the term nonresponse is used for not being able to obtain from some units selected in the sample. Among other reasons nonresponse may be due to the refusal to answer or due to evasive answers in response to a sensitive question. Warner (1965) presented the Randomized Response (RR) technique to estimate the proportion of respondents to a sensitive question without the knowledge of the respondents' personal status. This paper addresses the problem of optimum allocation in stratified sampling under RR model as an All Integer Nonlinear Programming Problem (AINLPP) in the presence of nonresponse. The solution to the formulated problem is obtained using optimization software.

scholarly journals A Biobjective Fuzzy Integer-Nonlinear Programming Approach for Creating an Intelligent Location-Aware Service

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Cheng Lin ◽  
Toly Chen
Keyword(s):  
Nonlinear Programming ◽  
Waiting Time ◽  
Dynamic Environment ◽  
The Other ◽  
Programming Approach ◽  
Just In Time ◽  
Nonlinear Programming Problem ◽  
Location Aware ◽  
Integer Nonlinear Programming ◽  
Service Location

An intelligent location-aware service is created in the present study, in which a timely service is provided to a user without changing the user's pace. To the user, there are two goals to achieve—one is to reach the service location just in time; the other is to get to the destination as soon as possible. To consider the two objectives at the same time and to allow for the uncertainty in the dynamic environment, a biobjective fuzzy integer-nonlinear programming problem is formulated and solved. To illustrate the applicability of the proposed methodology, an experiment has been performed. According to the experimental results, the user's waiting time was reduced by 61% using the proposed methodology.

scholarly journals gBoost: a mathematical programming approach to graph classification and regression

2008 ◽  
Vol 75 (1) ◽  
pp. 69-89 ◽  
Author(s):  
Hiroto Saigo ◽  
Sebastian Nowozin ◽  
Tadashi Kadowaki ◽  
Taku Kudo ◽  
Koji Tsuda
Keyword(s):  
Mathematical Programming ◽  
Programming Approach ◽  
Graph Classification ◽  
Mathematical Programming Approach ◽  
Classification And Regression

scholarly journals Formulating Intuitionistic Fuzzy Regression Models: A Mathematical Programming Approach

2021 ◽  
Author(s):  
Sheng-Hsing Nien ◽  
Liang-Hsuan Chen
Keyword(s):  
Mathematical Programming ◽  
Regression Models ◽  
Fuzzy Regression ◽  
Distance Measures ◽  
Programming Approach ◽  
Mathematical Programming Problem ◽  
Explanatory Variables ◽  
Intuitionistic Fuzzy ◽  
Mathematical Programming Approach ◽  
Ordinary Regression

Abstract This study develops a mathematical programming approach to establish intuitionistic fuzzy regression models (IFRMs) by considering the randomness and fuzziness of intuitionistic fuzzy observations. In contrast to existing approaches, the IFRMs are established in terms of five ordinary regression models representing the components of the estimated triangular intuitionistic fuzzy response variable. The optimal parameters of the five ordinary regression models are determined by solving the proposed mathematical programming problem, which is linearized to make the resolution process efficient. Based on the concepts of randomness and fuzziness in the formulation processes, the proposed approach can improve on existing approaches’ weaknesses with establishing IFRMs, such as the limitation of symmetrical triangular membership (or non-membership) functions, the determination of parameter signs in the model, and the wide spread of the estimated responses. In addition, some numerical explanatory variables included in the intuitionistic fuzzy observations are also allowed in the proposed approach, even though it was developed for intuitionistic fuzzy observations. In contrast to existing approaches, the proposed approach is general and flexible in applications. Comparisons show that the proposed approach outperforms existing approaches in terms of similarity and distance measures.

Sign in / Sign up

Export Citation Format

Share Document