Further Tests Based on a Product Multinomial Model: Order in the Sign Test and Ordinal Categorical Data with a Factorial Response

2000 ◽  
pp. 91-108
Keyword(s):  
Categorical Data ◽  
Sign Test ◽  
Multinomial Model ◽  
Model Order

Analyzing Stochastic Dependence of Cognitive Processes in Multidimensional Source Recognition

2014 ◽  
Vol 61 (5) ◽  
pp. 402-415 ◽  
Author(s):  
Thorsten Meiser
Keyword(s):  
Cognitive Processes ◽  
Model Complexity ◽  
Multinomial Model ◽  
Stochastic Dependence ◽  
New Model ◽  
Multinomial Processing Tree ◽  
Cognitive States ◽  
The Family ◽  
Tree Models ◽  
Flexible Framework

Stochastic dependence among cognitive processes can be modeled in different ways, and the family of multinomial processing tree models provides a flexible framework for analyzing stochastic dependence among discrete cognitive states. This article presents a multinomial model of multidimensional source recognition that specifies stochastic dependence by a parameter for the joint retrieval of multiple source attributes together with parameters for stochastically independent retrieval. The new model is equivalent to a previous multinomial model of multidimensional source memory for a subset of the parameter space. An empirical application illustrates the advantages of the new multinomial model of joint source recognition. The new model allows for a direct comparison of joint source retrieval across conditions, it avoids statistical problems due to inflated confidence intervals and does not imply a conceptual imbalance between source dimensions. Model selection criteria that take model complexity into account corroborate the new model of joint source recognition.

THE NEW ALGORITHM OF SOLVING HARMONIC BALANCE EQUATIONS

2019 ◽  
Author(s):  
Vladimir Lantsov ◽  
A. Papulina
Keyword(s):  
Harmonic Balance ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Balance Equations ◽  
Model Order ◽  
Cad Systems ◽  
Computational Costs ◽  
Reduction Methods ◽  
Model Equations

The new algorithm of solving harmonic balance equations which used in electronic CAD systems is presented. The new algorithm is based on implementation to harmonic balance equations the ideas of model order reduction methods. This algorithm allows significantly reduce the size of memory for storing of model equations and reduce of computational costs.

Non-Mode Clustering of Categorical Data with Attributes Weighting

2014 ◽  
Vol 24 (11) ◽  
pp. 2628-2641 ◽  
Author(s):  
Li-Fei CHEN ◽  
Gong-De GUO
Keyword(s):  
Categorical Data

Pengaruh Self Help Group Terhadap Kemampuan Keluarga Dalam Merawat Klien Skizofrenia Di Poli Jiwa Puskesmas Kalitidu

2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Yusuf Efendi ◽  
Errix Kristian Julianto
Keyword(s):  
Mental Disorders ◽  
Group Intervention ◽  
Family Members ◽  
Wilcoxon Test ◽  
Sign Test ◽  
Significance Level ◽  
Self Help ◽  
Before And After ◽  
Self Help Groups ◽  
Self Help Group

ABSTRAKDiera perkembangan jaman saat ini, beberapa keluarga dihadapkan dengan permasalahna tentang adanya angggota keluarga yeng mengaami gangguan jiwa, tak jarang keluarga tidak mengetahui bagaimana merawat angota keluarga dengan gangguan jiwa. Self help group pada keluarga dengan gangguan jiwa perlu dilakukan untuk membantu keluarga mengatasi permasalahannya yang diselesaikan bersama dalam kelompok. Manfaat yang didapatkan pada terapi ini adalah terdapatnya peningkatan pengetahuan keluarga tentang Skizofrenia. Peningkatan pengetahuan ini akan berdampak terhadap kemampuan keluarga dalam merawat klien Skizofrenia..Desain penelitian ini menggunakan desain pre eksperimental dengan rancangan one group pre-posttest design. Sampel pada penelitian ini adalah keluarga penderita Skizofrenia di PKU Jiwa Kalitidu yang berjumlah 32. . Data dikumpulkan menggunakan kuesioner kemudian dianalisis dengan menggunakan uji Wolcoxon sign dengan tingkat kemaknaan 0,05. Hasil penelitian menunjukkan bahwa kondisi responden sebelum dan sesudah dilakukan intervensi dengan self help group pada kemampuan merawat dengan  nilai uji wilcoxon sebesar 0,001 yang berarti ada pengaruh dari intervensi self help group dengan merawat keluarga dengan gangguan jiwa. Kata Kunci       : Self Help Group, Kemampuan Merawat, Skizofrenia   ABSTRACT. In the current era of development, some families are faced with problems about family members who suffer from mental disorders, often families do not know how to care for family members with mental disorders. Self help groups for families with mental disorders need to be done to help families overcome the problems that are solved together in a group. The benefit of this therapy is that there is an increase in family knowledge about Schizophrenia. This increase in knowledge will have an impact on the ability of families to care for Schizophrenia clients.The design of this study used a pre-experimental design with one group pre-posttest design. The sample in this study was the families of Schizophrenics in  Kalitidu public helath centre, amounting to 32.. Data were collected using a questionnaire and then analyzed using the Wolcoxon sign test with a significance level of 0.05.The results showed that the condition of the respondents before and after the intervention with self help group on the ability to care for Wilcoxon test value of 0.001, which means there is an influence of self help group intervention by caring for families with mental disorders. Keywords: Self Help Group, Caring Ability, Schizophrenia

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1020-1030
Author(s):  
Pradeep S. ◽  
Jagadish S. Kallimani
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Categorical Data ◽  
Numerical Data ◽  
Processing Technique ◽  
Machine Learning Algorithms ◽  
Sequential Data ◽  
Industry Standard ◽  
Robust Model ◽  
Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

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