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27
The unicorn, the normal curve, and other improbable creatures
 Psychological Bulletin
, 1989
"... An investigation of the distributional characteristics of 440 largesample achievement and psychometric measures found all to be significantly nonnormal at the alpha.01 significance level. Several classes of contamination were found, including tail weights from the uniform to the double exponential, ..."
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Cited by 41 (0 self)
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An investigation of the distributional characteristics of 440 largesample achievement and psychometric measures found all to be significantly nonnormal at the alpha.01 significance level. Several classes of contamination were found, including tail weights from the uniform to the double exponential, exponentiallevel asymmetry, severe digit preferences, multimodalities, and modes external to the mean/median interval. Thus, the underlying tenets of normalityassuming statistics appear fallacious for these commonly used types of data. However, findings here also fail to support the types of distributions used in most prior robustness research suggesting the failure of such statistics under nonnormal conditions. A reevaluation of the statistical robustness literature appears appropriate in light of these findings. 1 During recent years a considerable literature devoted to robust statistics has appeared. This research reflects a growing concern among statisticians regarding the robustness, or insensitivity, of parametric statistics to violations of their underlying assumptions. Recent findings suggest that the most commonly used of these statistics exhibit varying degrees of nonrobustness to certain violations of the normality assumption. Although the importance of such findings is underscored by numerous empirical studies documenting nonnormality in a variety of fields, a startling lack of such evidence exists for achievement
What Drives Capital Flows?, The Case of CrossBorder M&A Activity and Financial Deepening
 Journal of International Economics
, 2004
"... What macroeconomic and financial variables play key roles in the foreign direct investment decision (FDI) of firms? This question is addressed in this paper using a large panel data set of crossborder Merger & Acquisition (M&A) deals for the period 19901999. Various econometric specifications are ..."
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Cited by 33 (0 self)
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What macroeconomic and financial variables play key roles in the foreign direct investment decision (FDI) of firms? This question is addressed in this paper using a large panel data set of crossborder Merger & Acquisition (M&A) deals for the period 19901999. Various econometric specifications are built around the simple “gravity model ” commonly used in the trade literature. Interestingly, financial variables and other institutional factors seem to play a significant role in M&A flows. In particular the size of financial markets, as measured by the stock market capitalization to GDP ratio and the credit provided to the private sector by financial institutions to GDP ratio in the domestic economy, have sizeable positive effects on the incentives for domestic firms to invest abroad.
On the cost of data analysis
 Journal of Computational and Graphical Statistics
, 1992
"... A regression analysis usually consists of several stages such as variable selection, transformation and residual diagnosis. Inference is often made from the selected model without regard to the model selection methods that preceeded it. This can result in overoptimistic and biased inferences. We fir ..."
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Cited by 18 (2 self)
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A regression analysis usually consists of several stages such as variable selection, transformation and residual diagnosis. Inference is often made from the selected model without regard to the model selection methods that preceeded it. This can result in overoptimistic and biased inferences. We first characterize data analytic actions as functions acting on regression models. We investigate the extent of the problem and test bootstrap, jackknife and sample splitting methods for ameliorating it. We also demonstrate an interactive LISPSTAT system for assessing the cost of the data analysis while it is taking place.
An Analysis Of Transformations For Additive Nonparametric Regression
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 1997
"... ..."
Consistent Independent Component Analysis and Prewhitening
, 2005
"... We study the statistical merits of two techniques used in the literature of independent component analysis (ICA). First, we analyze the characteristicfunction based ICA method (CHFICA) and study its statistical properties such as consistency, consistency, and robustness against small additive noi ..."
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Cited by 13 (2 self)
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We study the statistical merits of two techniques used in the literature of independent component analysis (ICA). First, we analyze the characteristicfunction based ICA method (CHFICA) and study its statistical properties such as consistency, consistency, and robustness against small additive noise. Second, we study the validity of prewhitening: a preprocessing technique used by many ICA algorithms, as applied to the CHFICA method. In particular, we establish the surprising effectiveness of this technique even when some components have heavy tails and others do not. A fast new algorithm implementing the prewhitened CHFICA method is also provided.
Sure independence screening in generalized linear models with NPdimensionality. Revised for Ann. Statist
, 2009
"... Ultrahighdimensional variable selection plays an increasingly important role in contemporary scientific discoveries and statistical research. Among ..."
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Cited by 13 (5 self)
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Ultrahighdimensional variable selection plays an increasingly important role in contemporary scientific discoveries and statistical research. Among
The Cost of Adding Parameters to a Model
, 1996
"... For a general regression model with n independent observations we consider the variance of the estimate of a quantity of interest under two scenarios. One scenario is where all the parameters are estimated from the data, the other scenario is where a subset of the parameters are assumed known at the ..."
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Cited by 5 (1 self)
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For a general regression model with n independent observations we consider the variance of the estimate of a quantity of interest under two scenarios. One scenario is where all the parameters are estimated from the data, the other scenario is where a subset of the parameters are assumed known at their true values and the remaining parameters are estimated. We focus on quantities of interest which are defined on the scale of the response variable. We show that, under certain conditions, the ratio of a weighted sum across the design points of the variance of the quantity of interest is given by q=p, where q and p are the number of free parameters in the two scenarios. Thus, in this average sense, the inflation in variance associated with adding parameters, also interpreted as the cost of adding parameters to a model, is directly proportional to the number of parameters. We study models involving power transformations, nonlinear models and exponential family models. Key Words: BoxCox t...
Pediatric Pain, Predictive Inference and Sensitivity Analysis
 Evaluation Review
, 1994
"... The understanding, prevention and treatment of pain is of great importance to medical science. Children were asked to immerse their hands in cold water until they were unable to tolerate the pain of the cold. The length of time that they kept their hands immersed is a measure of pain tolerance. Two ..."
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Cited by 2 (2 self)
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The understanding, prevention and treatment of pain is of great importance to medical science. Children were asked to immerse their hands in cold water until they were unable to tolerate the pain of the cold. The length of time that they kept their hands immersed is a measure of pain tolerance. Two factors were studied; one factor is a child's Style of Coping (CS) with the pain (ATTENDERS pay attention to the pain, DISTRACTERS think of other things) and was assessed at a baseline trial. The other factor is Treatment (T), one of three counseling interventions (a NULL intervention, counseling to ATTEND, or counseling to DISTRACT) and was randomly applied prior to the response. The covariate is a baseline measure of pain tolerance prior to the intervention. Distracters taught to distract tolerated the pain much better than any other group. No strategy improved attenders pain tolerance. This paper analyzes this data from a predictive Bayesian viewpoint. The assumption of constant variance ...