Stat2 Building Models For A World Of Data Pdf Download
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However, not all authors agree that Big Data is a revolutionary phenomenon. Poynter [72] states Big Data will be more insightful in simply connecting the dots as opposed to painting a whole new picture. For [94], 2013 was the year for getting accustomed to Big Data and 2014 is the year for truly exploiting Big Data towards lucrative gains. We share and subscribe to [94] perception on Big Data. Accordingly, we present this review paper which aims to: provide an informative review of the forecasting techniques utilized for forecasting with Big Data; provide a concise summary of the contributions of yesteryear; and identify challenges which need be overcome as the world gears to embrace and live in the presence of Big Data. In the process, we are successful in reviewing a wide range of forecasting models which have been adopted for forecasting with Big Data. In order to enable the reader to have a clear understanding of the history, we present the review of applications differentiated by the relevant field (i.e., economics, finance, and energy among others) and topic where appropriate. Those interested in tools which can be used for manipulating Big Data are referred to Varian ([92], Table 1, p. 5)
Rey and Wells [75] believe Data Mining techniques can be exploited to help forecasting with Big Data, a view supported by [92]. However, it should be noted that in the past, Data Mining techniques have mainly been used on static data as opposed to time series (see for example [11, 39, 74]); [58]; [45]. Interestingly, [22] finds fault with Big Data for the recent financial crisis as he believes the financial models adopted were unable to handle the huge amounts of data that was being inputted into the systems, and thereby resulted in inaccurate forecasts.
In this section we focus mainly on the challenges which need be overcome when forecasting with Big Data. It is imperative to note that the availability of Big Data alone does not constitute the end of problems [4]. A good example is the existence of a vast amount of data on earthquakes, but the lack of a reliable model that can accurately predict earthquakes [84]. Some existing challenges are related to hypothesis, testing and models utilized for Big Data forecasting ([72, 84] whilst [95] identifies as an added concern, the lack of theory to complement Big Data. Besides these, we have identified the following varied challenges associated with forecasting Big Data that needs to be given due consideration.
The skills required for tackling the problem of forecasting with Big Data, and the availability of personnel skilled for this particular task is one of the foremost challenges. As [3] states, the advanced skills required to handle Big Data is a major challenge whilst [72] notes there is a short supply of data scientists equipped with the skills required to tackle Big Data. Thornton [90] also agrees that there is a shortage of people who can understand Big Data. In a world where academics, researchers and statisticians are highly experienced in using traditional statistical techniques for over fifty years to obtain accurate forecasts, the availability of Big Data in itself is challenging. Skupin and Agarwal [85] states that the inaptness of traditional statistical techniques which are meant for obtaining forecasts from traditional data are hindering the effectiveness and application of forecasts from Big Data and [3] shares this same concern. As majority of statisticians are experienced in these traditional techniques, [29] points out that it is a challenge to develop the required skills for Big Data forecasting. In order to overcome this issue, it is important that Higher Educational Institutes around the globe give due consideration to upgrading the educational syllabuses to incorporate the skills necessary for understanding, analysing, evaluating and forecasting with Big Data so that the next generation of statisticians will be well equipped with the mandatory skills.
Researchers in the field of Economics have been major exploiters of Big Data for forecasting various economic variables. Camacho and Sancho [17] used a dynamic factor model (DFM) based on the methodology presented in [87] to forecast a large dataset involving Spanish diffusion indexes which they describe as an exhaustive description of the Spanish economy. DFM models are an extension of [87] factor models and are frequently used for forecasting with Big Data. However, [24] asserted that the use of DFM for macroeconomic Big Data forecasting is flawed as it is based on linear models, and also as Big Data is more likely to be nonlinear. Over time through the work of [35, 88] and [54], the DFM technique was improved, thus enabling it to handle Big Data more appropriately.
This course is intended to build directly upon STAT 300 (Applied Statistical Modeling I) for students pursuing a major in statistics or a closely related program. Topics include likelihood-based inference, generalized linear models, random and mixed effects modeling, multilevel modeling. In particular, the applied nature of the course seeks to examine the advantages and disadvantages of various modeling tools presented, identify when they may be useful, use R software to implement them for analysis of real data, evaluate assumptions, interpret results, etc.
Identification of models for empirical data collected over time; use of models in forecasting. STAT 463 Applied Time Series Analysis (3)This course covers many major topics in time series analysis. Students will learn some theory behind various time series models and apply this theory to multiple examples. An introduction to time series and exploratory data analysis will be followed by a lengthy study of several important models, including autoregressive, moving average, autoregressive moving average (ARMA), autoregression integrated moving average (ARIMA), and seasonal models. For each model methods for parameter estimation, forecasting, and model diagnostics will be covered. Additional topics will include spectral techniques for periodic time series, including power spectra and the Fourier transform, and one or more miscellaneous topics chosen by the instructor, such as forecasting methods, transfer function models, multivariate time series methods, Kalman filtering, and signal extraction and forecasting. The use of statistical software will be a central component of this course, as will the proper interpretation of computer output. Students enrolling for this course are assumed to have taken a semester-long course on regression.
This is a capstone course intended primarily for undergraduate statistics majors in their last semester prior to graduation. The course is designed to reinforce problem solving and communication skills through development of writing ability, interaction with peers and the SCC, statistical consulting center (SCC), and oral presentations. Course objectives are tailored to the needs of each cohort and may include the application of statistical reasoning to real-world problems and case studies, recognition or recommendation of appropriate experimental designs, proficient use of ANOVA & GLMs with understanding of associated modeling assumptions, ability to identify concerns about the use or interpretation of statistical models in context, and both written and verbal communication of statistical findings.
This course teaches you how to analyze continuous response data and discrete count data. Linear regression, Poisson regression, negative binomial regression, gamma regression, analysis of variance, linear regression with indicator variables, analysis of covariance, and mixed models ANOVA are presented in the course.
In your introductory statistics course, you saw many facets of statistics but you probably did little if any work with the formal concept of a statistical model. To us, modeling is a very important part of statistics. In this book, we develop statistical models, building on ideas you encountered in your introductory course. We start by reviewing some topics from Stat 101 but adding the lens of modeling as a way to view ideas. Then we expand our view as we develop more complicated models.You will find a thread running through the book:
We hope that the Choose, Fit, Assess, Use quartet helps you develop a systematic approach to analyzing data.Modern statistical modeling involves quite a bit of computing. Fortunately, good software exists that enables flexible model fitting and easy comparisons of competing models. We hope that by the end of your Stat2 course, you will be comfortable using software to fit models that allow for deep understanding of complex problems.
When the practitioner is able to correctly incorporate sparsity into the model, it can improve all 3 interpretation desiderata. By reducing the number of parameters to analyze, sparse models can be easier to understand, yielding higher descriptive accuracy. Moreover, incorporating prior information in the form of sparsity into a sparse problem can help a model achieve higher predictive accuracy and yield more relevant insights. Note that incorporating sparsity can often be quite difficult, as it requires understanding the data-specific structure of the sparsity and how it can be modeled. 153554b96e