1,720,964 research outputs found
New discrimination procedure of location model for handling large categorical variables
No full textThe location model proposed in the past is a predictive discriminant rule that can classify new observations into one
of two predefined groups based on mixtures of continuous and categorical variables. The ability of location model to
discriminate new observation correctly is highly dependent on the number of multinomial cells created by the number
of categorical variables. This study conducts a preliminary investigation to show the location model that uses maximum
likelihood estimation has high misclassification rate up to 45% on average in dealing with more than six categorical
variables for all 36 data tested. Such model indicated highly incorrect prediction as this model performed badly for
large categorical variables even with large sample size. To alleviate the high rate of misclassification, a new strategy
is embedded in the discriminant rule by introducing nonlinear principal component analysis (NPCA) into the classical
location model (cLM), mainly to handle the large number of categorical variables. This new strategy is investigated
on some simulation and real datasets through the estimation of misclassification rate using leave-one-out method. The
results from numerical investigations manifest the feasibility of the proposed model as the misclassification rate is
dramatically decreased compared to the cLM for all 18 different data settings. A practical application using real dataset
demonstrates a significant improvement and obtains comparable result among the best methods that are compared. The
overall findings reveal that the proposed model extended the applicability range of the location model as previously it
was limited to only six categorical variables to achieve acceptable performance. This study proved that the proposed
model with new discrimination procedure can be used as an alternative to the problems of mixed variables classification,
primarily when facing with large categorical variables
Kajian Impirikal : Impak Faktor-Faktor Ekonomi Ke Atas Indeks Komposit Bursa Saham Kuala Lumpur (KLCI)
Get PDFAn economic climate is a major factor in determining the primary trend of a stock market. This study is conducted to examine empirically the impact of the economic factors such as interest, inflation, balance trade, total export and import, deficit balance of payment and national gross product on the Kuala Lumpur Stock Exchange Composite Index (KLCI). Another purpose of the study is to search the best
alternative mathematical model to explain the movement of the KLCI using the multiple linear regression method. In order to get the best alternative model, the first step is by performing the transformation for every independent variable so that it will have the linear relationship with the dependenit variable, KLCI. Four methods of
regression are using in determining the number of independent variable to be chosen which will make the largest contribution to the regression model. The problems such multicollinearity, heteroscedasticity and autocorrelation have been tested through variance inflation factor, figures and statistic Durbin-Watson.
The final model can be considered as the best alternative model since such problems have been minimized and about 99.8% of the variation in the KLCI can be explained
by four independent variables. Besides that, thus model has smaller standard error and mean square error much as compared to the other models that is 1.08E-06 and
1.347E-13. The significant economic factors that influence the KLCI movement include interest rate, inflation, total export, deficit balance of payment and the ratio
between balance of payment and total export. 'The result of the study showed that, interest rate and inflation do significantly and negatively affected the KLCI
Integrated smoothed location model and data reduction approaches for multi variables classification
Get PDFSmoothed Location Model is a classification rule that deals with mixture of continuous variables and binary variables simultaneously. This rule discriminates groups in a parametric form using conditional distribution of the continuous variables given each pattern of the binary variables. To conduct a practical
classification analysis, the objects must first be sorted into the cells of a multinomial table generated from the binary variables. Then, the parameters in each cell will be estimated using the sorted objects. However, in many situations, the estimated parameters are poor if the number of binary is large relative to the size of sample. Large binary variables will create too many multinomial cells which are empty, leading to high sparsity problem and finally give exceedingly poor performance for
the constructed rule. In the worst case scenario, the rule cannot be constructed. To
overcome such shortcomings, this study proposes new strategies to extract adequate variables that contribute to optimum performance of the rule. Combinations of two extraction techniques are introduced, namely 2PCA and PCA+MCA with new cutpoints of eigenvalue and total variance explained, to determine adequate extracted
variables which lead to minimum misclassification rate. The outcomes from these
extraction techniques are used to construct the smoothed location models, which then produce two new approaches of classification called 2PCALM and 2DLM. Numerical evidence from simulation studies demonstrates that the computed misclassification rate indicates no significant difference between the extraction
techniques in normal and non-normal data. Nevertheless, both proposed approaches are slightly affected for non-normal data and severely affected for highly overlapping groups. Investigations on some real data sets show that the two approaches are competitive with, and better than other existing classification methods. The overall findings reveal that both proposed approaches can be
considered as improvement to the location model, and alternatives to other classification methods particularly in handling mixed variables with large binary size
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
A new approach for classifying large number of mixed variables
Get PDFThe issue of classifying objects into one of predefined
groups when the measured variables are mixed with different types of variables has been part of interest among statisticians in many years. Some methods for dealing with such situation have been introduced that include parametric, semi-parametric and non-parametric approaches. This paper attempts to discuss on a problem in classifying a data when the number of measured mixed variables is
larger than the size of the sample.A propose idea that integrates a dimensionality reduction technique via principal component analysis and a discriminant function based on the location model is discussed. The study aims in offering practitioners another potential tool in a classification problem that is possible to be considered when the observed variables are mixed and too large
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