Encoder–Decoder Couplet Generation Model Based on ‘Trapezoidal Context’ Character Vector

2020 ◽  
Author(s):  
Rui Gao ◽  
Yuanyuan Zhu ◽  
Mingye Li ◽  
Shoufeng Li ◽  
Xiaohu Shi
Keyword(s):  
Real Data ◽  
Vector Model ◽  
Generation Model ◽  
First Line ◽  
Sequence Context ◽  
Data Set ◽  
Sequence Generation ◽  
Word Context ◽  
Proposed Model ◽  
Character Vector

Abstract This paper studies the couplet generation model which automatically generates the second line of a couplet by giving the first line. Unlike other sequence generation problems, couplet generation not only considers the sequential context within a sentence line but also emphasizes the relationships between the corresponding words of first and second lines. Therefore, a trapezoidal context character embedding the vector model has been developed firstly, which considers the ‘sequence context’ and the ‘corresponding word context’ simultaneously. Afterwards, we chose the typical encoder–decoder framework to solve the sequence–sequence problems, of which the encoder and decoder are used by bi-directional GRU and GRU, respectively. In order to further increase the semantic consistency of the first and second lines of couplets, the pre-trained sentence vector of the first line is added to the attention mechanism in the model. To verify the effectiveness of the method, it is applied to the real data set. Experimental results show that our proposed model can compete with the up-to-date methods, and both adding sentence vectors to attention and using trapezoidal context character vectors can improve the effectiveness of the algorithm.

EXPONENTIATED HALF-LOGISTIC LOMAX DISTRIBUTION WITH PROPERTIES AND APPLICATION

2019 ◽  
Vol XVI (2) ◽  
pp. 1-11
Author(s):  
Farrukh Jamal ◽  
Hesham Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Muhammad Akbar Ali Shah ◽  
Emrah Altun
Keyword(s):  
Real Data ◽  
Continuous Model ◽  
Model Parameters ◽  
Data Set ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Probability Weighted Moment ◽  
Record Statistics ◽  
Maximum Likelihood Criterion

A new three-parameter continuous model called the exponentiated half-logistic Lomax distribution is introduced in this paper. Basic mathematical properties for the proposed model were investigated which include raw and incomplete moments, skewness, kurtosis, generating functions, Rényi entropy, Lorenz, Bonferroni and Zenga curves, probability weighted moment, stress strength model, order statistics, and record statistics. The model parameters were estimated by using the maximum likelihood criterion and the behaviours of these estimates were examined by conducting a simulation study. The applicability of the new model is illustrated by applying it on a real data set.

scholarly journals Mixture of Inverse Weibull and Lognormal Distributions: Properties, Estimation, and Illustration

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
K. S. Sultan ◽  
A. S. Al-Moisheer
Keyword(s):  
Information Matrix ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
Component Mixture ◽  
Data Set ◽  
Hazard Functions ◽  
Lognormal Distributions ◽  
Proposed Model ◽  
Estimation Of Model Parameters

We discuss the two-component mixture of the inverse Weibull and lognormal distributions (MIWLND) as a lifetime model. First, we discuss the properties of the proposed model including the reliability and hazard functions. Next, we discuss the estimation of model parameters by using the maximum likelihood method (MLEs). We also derive expressions for the elements of the Fisher information matrix. Next, we demonstrate the usefulness of the proposed model by fitting it to a real data set. Finally, we draw some concluding remarks.

Multilevel Zero-inflated Censored Beta Regression Modeling for Proportions and Rate Data with Extra-zeros

2019 ◽  
Author(s):  
Leili Tapak ◽  
Omid Hamidi ◽  
Majid Sadeghifar ◽  
Hassan Doosti ◽  
Ghobad Moradi
Keyword(s):  
Regression Model ◽  
Simulation Study ◽  
Real Data ◽  
P Value ◽  
Parameter Estimates ◽  
Beta Regression ◽  
Rate Data ◽  
Data Set ◽  
Proposed Model ◽  
Beta Regression Model

Abstract Objectives Zero-inflated proportion or rate data nested in clusters due to the sampling structure can be found in many disciplines. Sometimes, the rate response may not be observed for some study units because of some limitations (false negative) like failure in recording data and the zeros are observed instead of the actual value of the rate/proportions (low incidence). In this study, we proposed a multilevel zero-inflated censored Beta regression model that can address zero-inflation rate data with low incidence.Methods We assumed that the random effects are independent and normally distributed. The performance of the proposed approach was evaluated by application on a three level real data set and a simulation study. We applied the proposed model to analyze brucellosis diagnosis rate data and investigate the effects of climatic and geographical position. For comparison, we also applied the standard zero-inflated censored Beta regression model that does not account for correlation.Results Results showed the proposed model performed better than zero-inflated censored Beta based on AIC criterion. Height (p-value <0.0001), temperature (p-value <0.0001) and precipitation (p-value = 0.0006) significantly affected brucellosis rates. While, precipitation in ZICBETA model was not statistically significant (p-value =0.385). Simulation study also showed that the estimations obtained by maximum likelihood approach had reasonable in terms of mean square error.Conclusions The results showed that the proposed method can capture the correlations in the real data set and yields accurate parameter estimates.

scholarly journals General Results for the Transmuted Family of Distributions and New Models

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Marcelo Bourguignon ◽  
Indranil Ghosh ◽  
Gauss M. Cordeiro
Keyword(s):  
Maximum Likelihood ◽  
Generating Functions ◽  
Real Data ◽  
Model Parameters ◽  
Data Set ◽  
Proposed Model ◽  
Method Of Maximum Likelihood ◽  
The Family ◽  
New Models ◽  
Family Of Distributions

The transmuted family of distributions has been receiving increased attention over the last few years. For a baselineGdistribution, we derive a simple representation for the transmuted-Gfamily density function as a linear mixture of theGand exponentiated-Gdensities. We investigate the asymptotes and shapes and obtain explicit expressions for the ordinary and incomplete moments, quantile and generating functions, mean deviations, Rényi and Shannon entropies, and order statistics and their moments. We estimate the model parameters of the family by the method of maximum likelihood. We prove empirically the flexibility of the proposed model by means of an application to a real data set.

scholarly journals Modifying the convexity condition in Data Envelopment Analysis (DEA)

2020 ◽  
Vol 33 (02) ◽  
pp. 454-467
Author(s):  
Roghyeh Malekii Vishkaeii ◽  
Behrouz Daneshian ◽  
Farhad Hosseinzadeh Lotfi
Keyword(s):  
Data Envelopment Analysis ◽  
Real Data ◽  
Data Envelopment ◽  
Convexity Condition ◽  
Discrimination Power ◽  
Data Set ◽  
Step Procedure ◽  
Proposed Model ◽  
Dea Models ◽  
Production Possibility Set

Conventional Data Envelopment Analysis (DEA) models are based on a production possibility set (PPS) that satisfies various postulates. Extension or modification of these axioms leads to different DEA models. In this paper, our focus concentrates on the convexity axiom, leaving the other axioms unmodified. Modifying or extending the convexity condition can lead to a different PPS. This adaptation is followed by a two-step procedure to evaluate the efficiency of a unit based on the resulting PPS. The proposed frontier is located between two standard, well-known DEA frontiers. The model presented can differentiate between units more finely than the standard variable return to scale (VRS) model. In order to illustrate the strengths of the proposed model, a real data set describing Iranian banks was employed. The results show that this alternative model outperforms the standard VRS model and increases the discrimination power of (VRS) models.

scholarly journals A Software Reliability Model Using Quantile Function

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Bijamma Thomas ◽  
Midhu Narayanan Nellikkattu ◽  
Sankaran Godan Paduthol
Keyword(s):  
Software Reliability ◽  
Real Data ◽  
Quantile Function ◽  
Data Set ◽  
Reliability Models ◽  
Software Reliability Models ◽  
Software Reliability Model ◽  
Reliability Characteristics ◽  
Proposed Model ◽  
L Moments

We study a class of software reliability models using quantile function. Various distributional properties of the class of distributions are studied. We also discuss the reliability characteristics of the class of distributions. Inference procedures on parameters of the model based on L-moments are studied. We apply the proposed model to a real data set.

scholarly journals On a new generalized lindley distribution: Properties, estimation and applications

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244328
Author(s):  
Ali Algarni
Keyword(s):  
Asymptotic Variance ◽  
Real Data ◽  
Lifetime Data ◽  
Unknown Parameters ◽  
Data Set ◽  
Lindley Distribution ◽  
New Family ◽  
Proposed Model ◽  
The Subject ◽  
Mean Square Error Criterion

In this study, an extension of the generalized Lindley distribution using the Marshall-Olkin method and its own sub-models is presented. This new model for modelling survival and lifetime data is flexible. Several statistical properties and characterizations of the subject distribution along with its reliability analysis are presented. Statistical inference for the new family such as the Maximum likelihood estimators and the asymptotic variance covariance matrix of the unknown parameters are discussed. A simulation study is considered to compare the efficiency of the different estimators based on mean square error criterion. Finally, a real data set is analyzed to show the flexibility of our proposed model compared with the fit attained by some other competitive distributions.

Marginal likelihood estimation of negative binomial parameters with applications to RNA-seq data

Biostatistics ◽  
2017 ◽  
Vol 18 (4) ◽  
pp. 637-650 ◽  
Author(s):  
Luis León-Novelo ◽  
Claudio Fuentes ◽  
Sarah Emerson
Keyword(s):  
Negative Binomial ◽  
Marginal Likelihood ◽  
Hypothesis Test ◽  
Likelihood Estimation ◽  
Real Data ◽  
Rna Seq ◽  
Data Set ◽  
Bayesian Hierarchical ◽  
Proposed Model ◽  
Bayesian Hypothesis Test

SUMMARY RNA-Seq data characteristically exhibits large variances, which need to be appropriately accounted for in any proposed model. We first explore the effects of this variability on the maximum likelihood estimator (MLE) of the dispersion parameter of the negative binomial distribution, and propose instead to use an estimator obtained via maximization of the marginal likelihood in a conjugate Bayesian framework. We show, via simulation studies, that the marginal MLE can better control this variation and produce a more stable and reliable estimator. We then formulate a conjugate Bayesian hierarchical model, and use this new estimator to propose a Bayesian hypothesis test to detect differentially expressed genes in RNA-Seq data. We use numerical studies to show that our much simpler approach is competitive with other negative binomial based procedures, and we use a real data set to illustrate the implementation and flexibility of the procedure.

Maintaining Dimension's History in Data Warehouses Effectively

2019 ◽  
Vol 15 (3) ◽  
pp. 46-62
Author(s):  
Canan Eren Atay ◽  
Georgia Garani
Keyword(s):  
Data Warehouse ◽  
Historical Data ◽  
Research Work ◽  
Real Data ◽  
Temporal Data ◽  
Data Warehouses ◽  
Data Set ◽  
Proposed Model ◽  
Object Relational ◽  
History Of

A data warehouse is considered a key aspect of success for any decision support system. Research on temporal databases have produced important results in this field, and data warehouses, which store historical data, can clearly benefit from such studies. A slowly changing dimension is a dimension in which any of its attributes in a data warehouse can change infrequently over time. Although different solutions have been proposed, each has its own particular disadvantages. The authors propose the Object-Relational Temporal Data Warehouse (O-RTDW) model for the slowly changing dimensions in this research work. Using this approach, it is possible to keep track of the whole history of an object in a data warehouse efficiently. The proposed model has been implemented on a real data set and tested successfully. Several limitations implied in other solutions, such as redundancy, surrogate keys, incomplete historical data, and creation of additional tables are not present in our solution.

scholarly journals Some Size Biased Probability Models for Adult out Migration Pattern

2020 ◽  
pp. 78-91
Author(s):  
Brijesh P. Singh ◽  
Sandeep Singh ◽  
Utpal Dhar Das ◽  
Gunjan Singh
Keyword(s):  
Maximum Likelihood ◽  
Poisson Distribution ◽  
Gamma Distribution ◽  
Real Data ◽  
Migration Pattern ◽  
Probability Models ◽  
Data Set ◽  
Proposed Model ◽  
Method Of Maximum Likelihood

In this paper an attempt has been made to inspect the distribution of the number of adult migrants from household through size biased probability models based on certain assumptions. Size Biased Poisson distribution compounded with various forms of Gamma distribution i.e. Gamma (1,θ) , Gamma (2,θ) and mixture of Gamma (1,θ) and Gamma (2,θ)  has been examined for some real data set of adult migration. The parameters of the proposed model have been estimated by method of maximum likelihood.  test indicates that the distributions proposed here are quite satisfactory to explain the pattern of adult out migration.

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