Statistics and Probability Commons^™

Power And Statistical Significance In Securities Fraud Litigation, Jill E. Fisch, Jonah B. Gelbach

All Faculty Scholarship

Event studies, a half-century-old approach to measuring the effect of events on stock prices, are now ubiquitous in securities fraud litigation. In determining whether the event study demonstrates a price effect, expert witnesses typically base their conclusion on whether the results are statistically significant at the 95% confidence level, a threshold that is drawn from the academic literature. As a positive matter, this represents a disconnect with legal standards of proof. As a normative matter, it may reduce enforcement of fraud claims because litigation event studies typically involve quite low statistical power even for large-scale frauds.

This paper, written for …

Analyzing Competitive Balance In Professional Sport, Kevin Alwell

Honors Scholar Theses

In this paper we review several measures to statistically analyze competitive balance and report which leagues have a wider variance of performance amongst its competitors. Each league seeks to maintain high levels of parity, making matches and overall season more unpredictable and appealing to the general audience. Here we quantify competitive advantage across major sports leagues in numbers using several statistical methods in order for leagues to optimize their revenue.

Measuring Gender Difference In Information Sharing Using Network Analysis: The Case Of The Austrian Interlocking Directorship Network In 2009, Carlo Drago, Livia Amidani Aliberti, Davide Carbonai

Carlo Drago

In recent literature a relevant problem has been the relationship between career/personal contact networks and different career paths. In addition the recent advances in social capital theory have shown the way in which networks impact on personal careers. In particular women’s careers appear to be negatively affected by the informational network structure. The main contribution of this work is to propose empirical evidence of this phenomenon by considering the gendered directorship network with relation to Austria and to show the structural differences by gender in the network. By using community detection techniques we have found various communities in which females …

A Note On The Indeterminacy And Arbitrariness Of Pena’S Method Of Construction Of Synthetic Indicators, Sudhanshu K. Mishra

Sudhanshu K Mishra

In this paper we demonstrate that Pena’s method of construction of a synthetic indicator is very sensitive to the order in which the constituent variables (whose linear aggregation yields the synthetic indicator) are arranged. Since m number of constituent variables may be arranged in m-factorial ways, even a moderately large m can give rise to a very large number of synthetic indicators from which one cannot choose the one which best represents the constituent variables. Given that an analyst has too little information as to the order in which a sizeable number of constituent variables must be arranged so as …

Empirical Methods-A Review: With An Introduction To Data Mining And Machine Learning, Matt Bogard

Economics Faculty Publications

This presentation was part of a staff workshop focused on empirical methods and applied research. This includes a basic overview of regression with matrix algebra, maximum likelihood, inference, and model assumptions. Distinctions are made between paradigms related to classical statistical methods and algorithmic approaches. The presentation concludes with a brief discussion of generalization error, data partitioning, decision trees, and neural networks.

Statistics For National Development, Sani I. Doguwa

CBN Journal of Applied Statistics (JAS)

Good statistics that has been collected according to agreed good practices are crucial as a tool for development. Gross domestic product (GDP) and other measures of economic activity such as Gross National Income (GNI) together with their individual components, show how the economy is responding to government policy and other influences. The balance of payments can demonstrate the requirement for policy adjustments and is also one of the indicators scrutinised by potential foreign investors in the country. Agricultural statistics clearly have implications for longer-term planning, particularly if they show a move away from the land into urban areas or a …

Big Macs And Eigenfactor Scores: Don't Let The Correlation Coefficients Fool You, Jevin D. West, Carl T. Bergstrom, Theodore C. Bergstrom

Ted C Bergstrom

A recent article by Phil Davis suggested that the Eigenvalue metric does adds little useful information to the more simply calculated measure of total citations published by the ISI. This paper argues that Davis's claim is an instance of a classic statistical fallacy of spurious correlation. Based on an analysis of the entire 2006 ISI Journal Citation Reports, we show that there are statistically and economically significant differences between the Eigenfactor metrics and the ISI's impact factor and total citations.

Temporal Changes In The Parameters Of Statistical Distribution Of Journal Impact Factor, Sudhanshu K. Mishra

Sudhanshu K Mishra

Statistical distribution of Journal Impact Factor (JIF) is characteristically asymmetric and non-mesokurtic. Even the distribution of log10(JIF) exhibits conspicuous skewness and non-mesokurticity. In this paper we estimate the parameters of Johnson SU distribution fitting to the log10(JIF) data for 10 years, 1999 through 2008, and study the temporal variations in those estimated parameters. We also study ‘over-the-samples stability’ in the estimated parameters for each year by the method of re-sampling close to bootstrapping. It has been found that log10(JIF) is Pearson-IV distributed. Johnson SU distribution fits very well to the data and yields parameters stable over the samples. We conclude …

Empirical Probability Distribution Of Journal Impact Factor And Over-The-Samples Stability In Its Estimated Parameters, Sudhanshu K. Mishra

Sudhanshu K Mishra

The data on JIFs provided by Thomson Scientific can only be considered as a sample since they do not cover the entire universe of those documents that cite an intellectual output (paper, article, etc) or are cited by others. Then, questions arise if the empirical distribution (best fit to the JIF data for any particular year) really represents the true or universal distribution, are its estimated parameters stable over the samples and do they have some scientific interpretation? It may be noted that if the estimated parameters do not exhibit stability over the samples (while the sample size is large …

A Note On Empirical Sample Distribution Of Journal Impact Factors In Major Discipline Groups, Sudhanshu K. Mishra

Sudhanshu K Mishra

What type of statistical distribution do the Journal Impact Factors follow? In the past, researchers have hypothesized various types of statistical distributions underlying the generation mechanism of journal impact factors. These are: lognormal, normal, approximately normal, Weibull, negative exponential, combination of exponentials, Poisson, Generalized inverse Gaussian-Poisson, negative binomial, generalized Waring, gamma, etc. It is pertinent to note that the major characteristics of JIF data lay in the asymmetry and non-mesokurticity. The present study, frequently encounters Burr-XII, inverse Burr-III (Dagum), Johnson SU, and a few other distributions closely related to Burr distributions to best fit the JIF data in subject groups …

Basic Statistics-I, Durgesh Chandra Pathak

Durgesh Chandra Pathak

It's a presentation that I prepared for JRF-students in Development Studies.

Modeling And Simulation Of Value -At -Risk In The Financial Market Area, Xiangyin Zheng

Doctoral Dissertations

Value-at-Risk (VaR) is a statistical approach to measure market risk. It is widely used by banks, securities firms, commodity and energy merchants, and other trading organizations. The main focus of this research is measuring and analyzing market risk by modeling and simulation of Value-at-Risk for portfolios in the financial market area. The objectives are (1) predicting possible future loss for a financial portfolio from VaR measurement, and (2) identifying how the distributions of the risk factors affect the distribution of the portfolio. Results from (1) and (2) provide valuable information for portfolio optimization and risk management.

The model systems chosen …

Statistical Section (June 1984), Central Bank Of Nigeria Cbn

Economic and Financial Review

Statistical tables on central banking, commercial banking, merchant banking, international liquidity, international trade, the flow of funds, money supply, national savings, production, consumer prices, public debt and public finance.

Statistical Section (September 1983), Central Bank Of Nigeria Cbn

Economic and Financial Review

The statistical tables comprised of the following: Central Bank of Nigeria statement of assets & liabilities, Central Bank monthly rediscounts gross, Commercial Banks' statement of assets and liabilities, Analysis of Commercial Banks' loans and advances, Selected predominant interest rates, Ratio of loans and advances to deposits, Net external assets of Commercial Banks, and Liquidity ratios of Commercial and merchants Banks'.

Statistical Section (June 1972), Central Bank Of Nigeria Cbn

Economic and Financial Review

The dataset set contains data from central banking, commercial banking, currency in circulation, external assets, international trade, money and capital, money supply, national savings, production and public debt covering 1966-1971.

Statistics For Decision Making, Central Bank Of Nigeria Cbn

Economic and Financial Review

The paper was reproduced for the Board of Trade Journal No 3783 of September 17, 1969. It was a speech from the Prime Minister on the need to for a change from the traditional system of industrial statistics to a more comprehensive scheme of quarterly reporting of production which will be supported by annual inquiries into such matters as purchases, fixed capital formation among others.

Statistical Section (June 1970), Central Bank Of Nigeria Cbn

Economic and Financial Review

The dataset include data for central bank and commercial banks statement of assets and liabilities, currency in circulation, external assets, international trade, money and capital markets, money supply, national savings, production, public debt and public finance.

Statistical Section (June 1969), Central Bank Of Nigeria Cbn

Economic and Financial Review

The dataset for the June 1969 issue of the Economic and Financial Review contents data for central banking, commercial banking, money supply, money and capital markets, public debt, national savings, external assets, international trade, production, fuel and power and public finance, covering data from 1962 to 1969.

Statistical Section (December 1968), Central Bank Of Nigeria Cbn

Economic and Financial Review

Publication of the statistics include analysis of commercial bank loans and advances, finance agricultural exports, money and capital marker, national savings, international trade, fuel and power and some new headings that are now included like transportation and communications, while some have been merged to other headings.

Statistical Section December 1966, Central Bank Of Nigeria Cbn

Economic and Financial Review

The statistics included in this publication for 1966 are the central bank statement of assets and liabilities, currency in circulation, commercial banking, money and capital markets, public finance, international trade, production, fuel and power among others

Statistical Section (June 1966), Central Bank Of Nigeria Cbn

Economic and Financial Review

This section of the EFR reports the statistics of the Central Bank of Nigeria (CBN) from 1960 to the first half 1966. This includes the Bank’s statement of assets and liabilities as well as its rediscount operations. Other sections include: currency in circulation, money supply, commercial banking activities, money and capital markets, public finance, national savings, external assets and international trade. The remaining sections include: Production (agricultural produce, mineral production), fuel and power, electricity generation and consumption.