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Results 1 - 10
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2,392
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
, 2001
"... Variable selection is fundamental to high-dimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized ..."
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Cited by 948 (62 self)
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likelihood approaches are proposed to handle these kinds of problems. The proposed methods select variables and estimate coefficients simultaneously. Hence they enable us to construct confidence intervals for estimated parameters. The proposed approaches are distinguished from others in that the penalty
The psychometric function: II. Bootstrap-based confidence intervals and sampling, Perception and Psychophysics 63
, 2001
"... The psychometric function relates an observer’s performance to an independent variable, usually a physical quantity of an experimental stimulus. Even if a model is successfully fit to the data and its goodness of fit is acceptable, experimenters require an estimate of the variability of the paramete ..."
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Cited by 116 (15 self)
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The psychometric function relates an observer’s performance to an independent variable, usually a physical quantity of an experimental stimulus. Even if a model is successfully fit to the data and its goodness of fit is acceptable, experimenters require an estimate of the variability
Spectral measures of risk: A coherent representation of subjective risk aversion
"... We study a space of coherent risk measures M / obtained as certain expansions of coherent elementary basis measures. In this space, the concept of ‘‘risk aversion function’ ’ / naturally arises as the spectral representation of each risk measure in a space of functions of confidence level probabili ..."
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Cited by 161 (1 self)
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We study a space of coherent risk measures M / obtained as certain expansions of coherent elementary basis measures. In this space, the concept of ‘‘risk aversion function’ ’ / naturally arises as the spectral representation of each risk measure in a space of functions of confidence level
Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000;56:337
"... SUMMARY. ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D ( t) , and ROC curves that vary as a function of time may be more appropriate. A common e ..."
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Cited by 140 (5 self)
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, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of ( X, T) , where T represents survival time (Akritas, M. J
Estimating the Confidence of Conditional Functional Dependencies
"... Conditional functional dependencies (CFDs) have recently been proposed as extensions of classical functional dependencies that apply to a certain subset of the relation, as specified by a pattern tableau. Calculating the support and confidence of a CFD (i.e., the size of the applicable subset and th ..."
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Cited by 10 (3 self)
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Conditional functional dependencies (CFDs) have recently been proposed as extensions of classical functional dependencies that apply to a certain subset of the relation, as specified by a pattern tableau. Calculating the support and confidence of a CFD (i.e., the size of the applicable subset
Remember–know: a matter of confidence
- Psychol. Rev
, 2004
"... This article critically examines the view that the signal detection theory (SDT) interpretation of the remember–know (RK) paradigm has been ruled out by the evidence. The author evaluates 5 empirical arguments against a database of 72 studies reporting RK data under 400 different conditions. These a ..."
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Cited by 72 (1 self)
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. These arguments concern (a) the functional independence of remember and know rates, (b) the invariance of estimates of sensitivity, (c) the relationship between remember rates and overall hit and false alarm rates, (d) the relationship between RK responses and confidence judgments, and (e) dissociations between
Functional data analysis for sparse longitudinal data.
- Journal of the American Statistical Association
, 2005
"... We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where the number of repeated measurements available per subject is small. In contrast, classical functional data a ..."
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Cited by 123 (24 self)
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conditioning step. This conditional estimation method is conceptually simple and straightforward to implement. A key step is the derivation of asymptotic consistency and distribution results under mild conditions, using tools from functional analysis. Functional data analysis for sparse longitudinal data
Whom You Know Matters: Venture Capital Networks and Investment Performance,
- Journal of Finance
, 2007
"... Abstract Many financial markets are characterized by strong relationships and networks, rather than arm's-length, spot-market transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company inv ..."
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Cited by 138 (8 self)
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investment opportunities. Consistent with the 22 It is difficult to control directly for exit market conditions over the life of a fund, as market conditions may vary widely over the 7+ years in which portfolio companies are likely to reach exit stage. The year fixed effects may help control
Nonlinear Dynamic Structures
- Econometrica
, 1993
"... We describe three methods for analyzing the dynamics of a nonlinear time series that is represented by a nonparametric estimate of its one-step ahead conditional density. These strategies are based on examination of conditional moment profiles corresponding to certain shocks; a conditional moment pr ..."
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Cited by 130 (10 self)
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We describe three methods for analyzing the dynamics of a nonlinear time series that is represented by a nonparametric estimate of its one-step ahead conditional density. These strategies are based on examination of conditional moment profiles corresponding to certain shocks; a conditional moment
Nonparametric Density Estimation and Tests of Continuous Time Interest Rate Models
- Review of Financial Studies
, 1998
"... A number of recent papers have used nonparametric density estimation or nonparametric regression to study the instantaneous spot interest rate, and to test term structure models. However, little is known about the performance of these methods when applied to persistent time-series, such as U.S. inte ..."
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Cited by 105 (2 self)
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result is that the test rejects too often when using asymptotic critical values and 22 years of data. The reason for the high rejection rate is probably because the asymptotic distribution of the test does not depend on persistence, but the nite sample performance of the estimator does. After critical
Results 1 - 10
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2,392
