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Laha, Arnab Kumar
- Interval-Valued Data Analysis
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Affiliations
1 Indian Institute of Management, Ahmedabad, Gujarat, IN
1 Indian Institute of Management, Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 3, No 2 (2015), Pagination: 1-3Abstract
Interval-valued data arise in real-life in many different ways. Weather data published daily in newspapers contains only the maximum and minimum temperature readings during a day for a city, stock market data contains the highest and lowest traded price of a stock or a stockindex on a day etc. In the Big Data context often data are aggregated for certain features of interest giving rise to interval-valued data.
- Analyzing Data Which are Curves-Functional Data Analysis
Abstract Views :298 |
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Authors
Affiliations
1 Indian Institute of Management Ahmedabad, Gujarat, IN
1 Indian Institute of Management Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 4, No 2 (2016), Pagination: 1-4Abstract
In many areas of application, the data comes in the form of a curve or in other words a function. Consider a hotel having a fixed number of rooms. Suppose the hotel starts accepting booking for one year in advance. The information about the number of rooms booked for a particular date (say for 1st April, 2016) would be available for every day in the period 1st April, 2015 to 31st March, 2016. If the number of rooms booked 't' days prior to 1st April 2016 is denoted by b(t) then the curve {b(t) : 0≤ t ≤ 365} is called the booking curve for 1st April 2016. Similar curves would be available for the number of rooms booked for 2nd April, 3rd April etc. Thus we would have the data in the form of curves {b1, b2,..., bn}. Statistical analysis of this kind of data is referred to as functional data analysis. One of the simplest questions to ask with booking curves data is about the shape of the average booking curve.- Distribution of Traffic Accident Times in India - Some Insights Using Circular Data Analysis
Abstract Views :250 |
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Authors
Affiliations
1 Indian Institute of Management, Ahmedabad, Gujarat, IN
2 Saurashtra University, Rajkot, Gujarat, IN
1 Indian Institute of Management, Ahmedabad, Gujarat, IN
2 Saurashtra University, Rajkot, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 5, No 1 (2017), Pagination: 26-35Abstract
Traffic accidents are a major hazard for travellers on Indian roads. These are caused by a variety of reasons including the bad condition of roads, traffic density, lack of proper training of drivers, slack in enforcement of traffic rules, poor road lighting etc. It is further known that certain times of the day are more prone to traffic accidents than others. In this paper we investigate the distribution of traffic accident times using the data published annually by the National Crime Records Bureau (NCRB) over the period 2001-2014 using the tools of circular data analysis. It is seen that the observed distribution of the traffic accident times in most years is bimodal. Thus, several modelling strategies for bimodal distributions are tried which include fitting of mixture of von-Mises distributions and mixture of Kato-Jones distribution. It is seen from this analysis that the distribution of the traffic accident times are changing over the years. Notably, the proportion of accidents happening in late night has reduced over the years while the same has increased for late evening hours. Some more insights obtained from this analysis are also discussed.Keywords
Circular Statistics, Kato-Jones Distribution, Mixture Distribution, Traffic Accidents, Von-Mises Distribution.References
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- Brunsdon, C., & Corcoran, J. (2006). Using circular statistics to analyse time patterns in crime incidence. Computers, Environment and Urban Systems, 30, 300-319.
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- Cools, M., Moons, E., & Wets, G. (2010). Assessing the impact of weather on traffic intensity. American Meteorological Society, Weather, Climate and Society, 2, 60-68.
- Corcoran, J. , Chhetri, P., & Stimson, R. (2009). Using circular statistics to explore the geography of the journey to work, Progress in Regional Science, 88(1), 119-132.
- David, M. B, & Hyder, A. A. (2006). Modelling the cost effectiveness of injury interventions in lower and middle income countries: Opportunities and challenges. Cost Effectiveness and Resource Allocation, 4, 2, 1-11.
- Faggian, A., Corcoran, J., & McCann, P. (2013). Modelling geographical graduate job search using circular statistics, Papers in regional Science, 92(2), 329-343.
- Ghosh, K., Jammalamadaka, S. R., & Vasudaven, M. (1999). Change-point problems for the von-Mises distribution. Journal of applied statistics, 26(4), 423-434.
- Gill, J., & Hangartner, D. (2010). Circular data in Political Science and how to handle It, Political Analysis, 18 (3), 316-336.
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- Jiang, Q. (2009). On fitting a mixture of two von-Mises distributions, with applications. M.Sc Project, Department of Statistics and Actuarial Science, Simon Fraser University, Canada.
- Kato, S., & Jones, M. C. (2010). A family of distributions on the circle with links to, and applications arising from mobius transformation. Journal of American Statistical Association, 105 (489), 249-262.
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- Sengupta, A., & Laha, A. K. (2008a). A likelihood integrated method for exploratory graphical analysis of change point problem with directional data. Communications in statistics. Theory and methods, 37(11-12), 1783-1791.
- Sengupta, A., & Laha, A. K. (2008b). A Bayesian analysis of the change-point problem for directional data. Journal of Applied Statistics, 35(6), 693-700.
- von Mises, R. (1918). UJber die ‘Ganzzahligheit’ der Atomgewichte und vermandteFragen. Phys. Z. 19, 490-500.
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- Analysis of Data Streams
Abstract Views :320 |
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Authors
Affiliations
1 Indian Institute of Management, Ahmedabad, Gujarat, IN
1 Indian Institute of Management, Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 5, No 2 (2017), Pagination: 1-2Abstract
One of the four Vs of Big data is 'velocity' which refers to the fact that in many applications, data is not static but continuously flows into the system (often at a very high rate). Such continuously flowing data is termed as Streaming data and is generated by various sources such as surveillance cameras, sensors in machines such as aircraft engines, tractors, vehicles and mobile phones, atmospheric systems, mass production systems, transactions such as those of a credit card system etc. In some applications such as credit card fraud detection, intrusion detection or preventive maintenance it is important that we are able to analyse the data stream in real time.- Analytically Yours:On Tails
Abstract Views :356 |
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Authors
Affiliations
1 Indian Institute of Management, Ahmedabad, Gujarat, IN
1 Indian Institute of Management, Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 6, No 1 (2018), Pagination: 1-3Abstract
This article is about tails. Not only animals have tails, but probability distributions have tails too. We will be discussing the tails of probability distributions in this article. In recent times, we have heard about different kinds of tails of probability distributions such as light tail, heavy tail, and long tail. What are these and what should we know about them? This article aims to give a glimpse.- Perspective:When Data are Ranks-Analysis of Rank Data
Abstract Views :199 |
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Authors
Affiliations
1 Indian Institute of Management Ahmedabad, Gujarat, IN
1 Indian Institute of Management Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 6, No 2 (2018), Pagination: 2-5Abstract
In many real life situations respondents are asked to rank order a set of items based on their preferences. This can happen in selection interviews where a set of candidates have to be rank ordered (say, from best to worst) regarding their suitability for a job or position or in boardroom discussions where different alternative investment proposals have to be ranked based on their risk-reward profiles. In many market research studies respondents are asked to rank order a set of items with respect to their possibility of buying them. Thus rank data occur quite commonly in our daily life.References
- Kemeny, J. G., & Snell, L. J. (1962). Preference ranking: An axiomatic approach, in mathematical models in the social sciences. Ginn., New York, 9-23.
- Laha, A. K., & Dongaonkar, S. (2012). Bayesian analysis of rank data using SIR. In SenGupta, A. editor, Advances in Multivariate Statistical Methods, chapter 19, pp. 327-335. World Scientific.
- Laha, A., Dutta, S., & Roy, V. (2017). A novel sandwich algorithm for empirical Bayes analysis of rank data. Statistics and Its Interface, 10(4), 543-556.
- Mallows, C. L. (1957). Non-null ranking models. I. Biometrika, 44(1/2), 114-130.
- One Data, Many Tests
Abstract Views :232 |
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Authors
Affiliations
1 Indian Institute of Management Ahmedabad, Gujarat, IN
1 Indian Institute of Management Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 7, No 1 (2019), Pagination: 1-2Abstract
In many areas of research in management, social science, medical science, genomics, business studies and psychology it is a common practice for researchers to test their theoretical understanding of a phenomenon through formulation of appropriate hypotheses which can be proved or disproved on the basis of data. These researchers argue that if the data provides support to the formulated hypotheses then it can be concluded that the theory based on which these hypotheses were derived is also empirically validated. Typically a piece of research may depend on testing more than one hypothesis and the empirical validation of the theory requires all these hypotheses being supported.References
- Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289-300.
- Analytically Yours:Measures of Association Among Several Variables
Abstract Views :307 |
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Authors
Affiliations
1 Indian Institute of Management Ahmedabad, Gujarat, IN
1 Indian Institute of Management Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 7, No 2 (2019), Pagination: 1-3Abstract
No Abstract.Keywords
No Keywords.References
- Eaton, M. L. (2007). Multivariate statistics: A vector space approach, 53, IMS Lecture Notes Monograph Series.
- Escoufier, Y. (1973). Le traitement des variables vectorielles. Biometrics, 29, 751-760.
- Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28, 321-377.
- Josse, J., & Holmes, S. (2016). Measuring multivariate association and beyond. Statistics Surveys, 10, 132-167.
- Székely, G. J., Rizzo, M. L., & Bakirov, N. K (2007). Measuring and testing dependence by correlation of distances. Annals of Statistics, 35(6), 2769-2794.
- Analytically Yours:Spatial Data Analysis
Abstract Views :359 |
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Authors
Affiliations
1 Indian Institute of Management Ahmedabad, Gujarat, IN
1 Indian Institute of Management Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 8, No 1 (2020), Pagination: 1-3Abstract
No Abstract.Keywords
No Keywords.- The Cause of the Effect
Abstract Views :111 |
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Authors
Affiliations
1 Indian Institute of Management Ahmedabad, Gujarat, IN
1 Indian Institute of Management Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 8, No 2 (2020), Pagination: 1-3Abstract
No Abstract.Keywords
No Keywords.References
- Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Allen Lane.
- Beyond the Pie Diagram : Analysing Compositional Data
Abstract Views :113 |
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Authors
Affiliations
1 Indian Institute of Management Ahmedabad, Gujarat, IN
1 Indian Institute of Management Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 9, No 1&2 (2021), Pagination: 1-5Abstract
No Abstract.Keywords
No Keywords.References
- Filzmoser, P., Hron, K., & Templ, M. (2018). Applied compositional data analysis with worked examples in R. Springer.
- Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2015). Modeling and analysis of compositional data. Wiley.
- Analytically Yours : Information and Dependence
Abstract Views :240 |
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Authors
Affiliations
1 Indian Institute of Management Ahmedabad, Gujarat, IN
1 Indian Institute of Management Ahmedabad, Gujarat, IN
Source
International Journal of Business Analytics and Intelligence, Vol 10, No 1 (2022), Pagination: 1-3Abstract
No Abstract.Keywords
No Keywords.References
- Linfoot, E. H. (1957). An informational measure of correlation. Information and Control, 1, 85-89.
- Cover, T. M., & Thomas, J. A. (2006). Elements of information theory (2nd ed.). Wiley - Interscience.