POISSON REGRESSION MODEL PREDICTION ON THE VALUE OF ACADEMIC PUBLICATION
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feasible implementation to predict the future outcomes is by using a poisson regression model to forecast the quantity of research publication which is supported by the time spent in developing research and the number of years experienced by an academician. Poisson regression method is a classification of General Linear models where the variable under observation follows a poisson distribution with discrete variables making it identical to poisson regression model. Research is a skill to be practiced; however there are numerous components of this skill that must be directed, so potential knowledge makers can navigate contradictory, unpredicted and sensitive situation successfully.Keywords:
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The development of models that go beyond traditional linear regression has been a topic of great interest in statistical research over the last years. As a consequence, a powerful toolbox has emerged, allowing for a realistic modelling of a variety of real data problems. This thesis presents several applications of modern regression techniques and addresses various issues that are currently being discussed in the economics literature. In the first analysis, we semi-parametrically model conditional quantiles of farmland rental rates using Bayesian geoadditive quantile regression. The second analysis investigates the multifaceted dimension of upward social mobility in the United States by modelling all parameters of a multivariate response distribution as a function of covariates employing Bayesian distributional regression. The third analysis is concerned with the optimality of investment decisions and explores the influencing factors on the timing of investment decisions applying Generalised Additive Mixed Models. The results of our analyses are of potential interest for academics and policymakers since the use of advanced regression models allows for revealing additional information in the data that will remain undetected if more traditional models are used.
Quantile regression
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Abstract : The report was written to give the scientist, engineer or laboratory manager who has a limited background in the area of statistics a better understanding of the methods of correlation and regression. A number of examples have been given to illustrate the methods that have been developed, together with numerous graphical representations to give the reader a pictorial description of many interlocking relationships. No prior knowledge of statistics is assumed, but some experience in mathematics beyond calculus is necessary to fully comprehend the theoretical development. No attempt has been made in this article to discuss such topics as confidence intervals or tests of hypotheses involving the correlation coefficient or regression equation. While these topics are certainly important statistical concepts, it was considered desirable to restrict the scope of the text to interpretations of the principles of correlation and regression.
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Density regression models allow the conditional distribution of the response given predictors to change flexibly over the predictor space. Such models are much more flexible than nonparametric mean regression models with nonparametric residual distributions, and are well supported in many applications. A rich variety of Bayesian methods have been proposed for density regression, but it is not clear whether such priors have full support so that any true data-generating model can be accurately approximated. This article develops a new class of density regression models that incorporate stochastic-ordering constraints which are natural when a response tends to increase or decrease monotonely with a predictor. Theory is developed showing large support. Methods are developed for hypothesis testing, with posterior computation relying on a simple Gibbs sampler. Frequentist properties are illustrated in a simulation study, and an epidemiology application is considered.
Isotonic regression
Conditional probability distribution
Gibbs sampling
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Applied statistics in research have an important role to play in the collection, compilation, analysis and interpretation of the data. In view of the day to day rapid changes in the research spectrum, the scenario is becoming interesting for a researcher. A Model is defined as abstraction of real situations, which aim to give the empirical content to relationships of variable and their interpretation. Modeling techniques are very common in basic as well as multidisciplinary research. This paper discusses the modeling and regression techniques for specific circumstances. Regression analysis technique explains the importance of variables and amount of change in exogenous variables if explanatory variables change with one unit. In this paper also describes the multiple and limited dependent variables especially logistic regression
Regression diagnostic
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Usually scientists breed research ideas inspired by previous publications, but they are unlikely to follow all publications in the unbounded literature collection. The volume of literature keeps on expanding extremely fast, whilst not all papers contribute equal impact to the academic society. Being aware of potentially influential literature would put one in an advanced position in choosing important research references. Hence, estimation of potential influence is of great significance. We study a challenging problem of identifying potentially influential literature. We examine a set of hypotheses on what are the fundamental characteristics for highly cited papers and find some interesting patterns. Based on these observations, we learn to identify potentially influential literature via Future Influence Prediction (FIP), which aims to estimate the future influence of literature. The system takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period. We consider several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R2). Experimental results on a real-large data set show a mean average predictive performance of 83.6% measured in R^2. We apply the learned model to the application of bibliography recommendation and obtain prominent performance improvement in terms of Mean Average Precision (MAP).
Position (finance)
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THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATANOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data. Features of the Second Edition include: Expanded coverage of interactions and the covariate-adjusted survival functions The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques New discussion of variable selection with multivariable fractional polynomials Further exploration of time-varying covariates, complex with examples Additional treatment of the exponential, Weibull, and log-logistic parametric regression models Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values New examples and exercises at the end of each chapter Analyses throughout the text are performed using Stata Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.
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Researchers in the social sciences business policy studies and other areas rely heavily on the use of linear regression analysis. This manual provides the background needed to understand much of the empirical work relying on linear regression analysis; it explains its basic procedures and terms. Written at an elementary level and assuming only a minimal mathematics background this book focuses on the intuitive and verbal interpretation of regression coefficients associated statistics and hypothesis tests. The manual also explains the terminology often encountered in the literature such as standardized regression coefficients dummy variables interaction terms and transformations. This book can be used as a text in a variety of courses in different disciplines. Examples are drawn from demography economics education finance marketing policy analysis political science public administration and sociology. This manual does not substitute for a statisics course; it does not teach the use of regression analysis. Rather it is intended to fill the void that exists when a student studies empirical papers before taking a statistics course. However the volumes level does make it suitable as a supplementary text for a statistics course. Chapters cover linear regression multiple linear regression hypothesis testing extensions to multiple regression models and problems and issues in linear regression. Appendices contain derivation of a and b critical values for a t distribution regression output from SAS and SPSS and suggested texts.
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Predicting the scientific productivity of researchers is a basic task for academic administrators and funding agencies. This study provided a model for the publication dynamics of researchers, inspired by the distribution feature of researchers' publications in quantity. It is a piecewise Poisson model, analyzing and predicting the publication productivity of researchers by regression. The principle of the model is built on the explanation for the distribution feature as a result of an inhomogeneous Poisson process that can be approximated as a piecewise Poisson process. The model's principle was validated by the high quality dblp dataset, and its effectiveness was testified in predicting the publication productivity for majority of researchers and the evolutionary trend of their publication productivity. Tests to confirm or disconfirm the model are also proposed. The model has the advantage of providing results in an unbiased way; thus is useful for funding agencies that evaluate a vast number of applications with a quantitative index on publications.
Feature (linguistics)
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This workshop introduces students to current methods for analyzing categorical data, with its principal focus being regression models for categorical outcomes. We will consider models for binary, ordinal, and nominal outcomes, as well as useful and related models for censored and count outcomes. We will discuss the appropriate specification of models, their estimation with statistical software, and the proper and practical interpretation. Computing in the course will primarily use Stata. The course assumes a good working knowledge of the linear regression model for continuous variables, as well as an elementary knowledge of matrix algebra.
Categorical variable
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Understanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong. Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations. Hence, the emphasis is to model Nature’s processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways.
Key features of the book include:
Numerous worked examples using the R software
Key points and self-study questions displayed just-in-time within chapters
Simple mathematical explanations (baby proofs) of key concepts
Clear explanations and applications of statistical significance (p-values), incorporating the American Statistical Association guidelines
Use of data-generating process terminology rather than population
Random-X framework is assumed throughout (the fixed-X case is presented as a special case of the random-X case)
Clear explanations of probabilistic modelling, including likelihood-based methods
Use of simulations throughout to explain concepts and to perform data analyses
This book has a strong orientation towards science in general, as well as chapter-review and self-study questions, so it can be used as a textbook for research-oriented students in the social, biological and medical, and physical and engineering sciences. As well, its mathematical emphasis makes it ideal for a text in mathematics and statistics courses. With its numerous worked examples, it is also ideally suited to be a reference book for all scientists.
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