1,278 research outputs found
Generalized Method of Moments Estimator Based On Semiparametric Quantile Regression Imputation
In this article, we consider an imputation method to handle missing response
values based on semiparametric quantile regression estimation. In the proposed
method, the missing response values are generated using the estimated
conditional quantile regression function at given values of covariates. We
adopt the generalized method of moments for estimation of parameters defined
through a general estimation equation. We demonstrate that the proposed
estimator, which combines both semiparametric quantile regression imputation
and generalized method of moments, has competitive edge against some of the
most widely used parametric and non-parametric imputation estimators. The
consistency and the asymptotic normality of our estimator are established and
variance estimation is provided. Results from a limited simulation study and an
empirical study are presented to show the adequacy of the proposed method
Parameter estimation and model testing for Markov processes via conditional characteristic functions
Markov processes are used in a wide range of disciplines, including finance.
The transition densities of these processes are often unknown. However, the
conditional characteristic functions are more likely to be available,
especially for L\'{e}vy-driven processes. We propose an empirical likelihood
approach, for both parameter estimation and model specification testing, based
on the conditional characteristic function for processes with either continuous
or discontinuous sample paths. Theoretical properties of the empirical
likelihood estimator for parameters and a smoothed empirical likelihood ratio
test for a parametric specification of the process are provided. Simulations
and empirical case studies are carried out to confirm the effectiveness of the
proposed estimator and test.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ400 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Reciprocal Recommendation System for Online Dating
Online dating sites have become popular platforms for people to look for
potential romantic partners. Different from traditional user-item
recommendations where the goal is to match items (e.g., books, videos, etc)
with a user's interests, a recommendation system for online dating aims to
match people who are mutually interested in and likely to communicate with each
other. We introduce similarity measures that capture the unique features and
characteristics of the online dating network, for example, the interest
similarity between two users if they send messages to same users, and
attractiveness similarity if they receive messages from same users. A
reciprocal score that measures the compatibility between a user and each
potential dating candidate is computed and the recommendation list is generated
to include users with top scores. The performance of our proposed
recommendation system is evaluated on a real-world dataset from a major online
dating site in China. The results show that our recommendation algorithms
significantly outperform previously proposed approaches, and the collaborative
filtering-based algorithms achieve much better performance than content-based
algorithms in both precision and recall. Our results also reveal interesting
behavioral difference between male and female users when it comes to looking
for potential dates. In particular, males tend to be focused on their own
interest and oblivious towards their attractiveness to potential dates, while
females are more conscientious to their own attractiveness to the other side of
the line
United States and European Union Approaches to Internet Jurisdiction and Their Impact on E-Commerce
United States and European Union Approaches to Internet Jurisdiction and Their Impact on E-Commerce
Left Behind? Migration Stories of Two Women in Rural China
Women being left behind in the countryside by husbands who migrate to work has been a common phenomenon in China. On the other hand, over time, rural women’s participation in migration has increased precipitously, many doing so after their children are older, and those of a younger generation tend to start migrant work soon after finishing school. Although these women may no longer be left behind physically, their work, mobility, circularity, and frequency of return continue to be governed by deep-rooted gender ideology that defines their role primarily as caregivers. Through the biographical stories of two rural women in Anhui, this article shows that traditional gender norms persist across generations. Yingyue is of an older generation and provided care to her husband, children, and later grandchildren when she was left behind, when she participated in migration, and when she returned to her village. Shuang is 30 years younger and aspires to urban lifestyle such as living in apartments and using daycare for her young children. Yet, like Yingyue, Shuang’s priority is caregiving. Her decisions, which are in tandem with her parents-in-law, highlight how Chinese families stick together as a safety net. Her desire to earn wages, an activity much constrained by her caregiving responsibility to two young children, illustrates a strong connection between income-generation ability and identity among women of the younger generation. These two stories underscore the importance of examining how women are left behind not only physically but in their access to opportunities such as education and income-generating activity
Minimal Permutations and 2-Regular Skew Tableaux
Bouvel and Pergola introduced the notion of minimal permutations in the study
of the whole genome duplication-random loss model for genome rearrangements.
Let denote the set of minimal permutations of length
with descents, and let . They derived that
and , where is the -th
Catalan number. Mansour and Yan proved that . In
this paper, we consider the problem of counting minimal permutations in
with a prescribed set of ascents. We show that such
structures are in one-to-one correspondence with a class of skew Young
tableaux, which we call -regular skew tableaux. Using the determinantal
formula for the number of skew Young tableaux of a given shape, we find an
explicit formula for . Furthermore, by using the Knuth equivalence,
we give a combinatorial interpretation of a formula for a refinement of the
number .Comment: 19 page
Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments
Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment
Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm
Industry 4.0 aims at achieving mass customization at a
mass production cost. A key component to realizing this is accurate
prediction of customer needs and wants, which is however a
challenging issue due to the lack of smart analytics tools. This
paper investigates this issue in depth and then develops a predictive
analytic framework for integrating cloud computing, big data
analysis, business informatics, communication technologies, and
digital industrial production systems. Computational intelligence
in the form of a cluster k-means approach is used to manage
relevant big data for feeding potential customer needs and wants
to smart designs for targeted productivity and customized mass
production. The identification of patterns from big data is achieved
with cluster k-means and with the selection of optimal attributes
using genetic algorithms. A car customization case study shows
how it may be applied and where to assign new clusters with
growing knowledge of customer needs and wants. This approach
offer a number of features suitable to smart design in realizing
Industry 4.0
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