Search Results - "Monrad, Ditlev"
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Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings
Published in Neuropsychopharmacology (New York, N.Y.) (01-06-2021)“…Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a…”
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Small values of Gaussian processes and functional laws of the iterated logarithm
Published in Probability theory and related fields (01-06-1995)Get full text
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Nearby variables with nearby conditional laws and a strong approximation theorem for Hilbert space valued martingales
Published in Probability theory and related fields (01-09-1991)Get full text
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Limit Theorems for Stochastic Processes
Published in Journal of the American Statistical Association (01-12-1988)Get full text
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Asymmetric Cauchy Processes: Sample Functions at Last Zero
Published in The Annals of probability (01-10-1978)“…For asymmetric Cauchy processes an integral test is given for the sample function growth immediately after the process has been zero for the last time…”
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Stable Processes: Sample Function Growth at a Last Exit Time
Published in The Annals of probability (01-06-1977)“…For stable processes of index $\alpha, 1 < \alpha < 2$, exact upper functions are determined for the sample function growth at a last exit time…”
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Jacod, J., and Shiryaev, A. N., "Limit Theorems for Stochastic Processes" (Book Review)
Published in Journal of the American Statistical Association (01-01-1988)Get full text
Book Review -
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Gaussian Measures in $B_p
Published in The Annals of probability (01-02-1983)“…For p \geq 1, conditions for a separable Gaussian process to have sample paths of finite p-variation are given in terms of the mean function and the covariance…”
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Gaussian Measures in Bp
Published in The Annals of probability (01-02-1983)“…For p ≥ 1, conditions for a separable Gaussian process to have sample paths of finite p-variation are given in terms of the mean function and the covariance…”
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