Faculty

Institution

Unit

Venue

Nalini Ravishanker

Professor

University of Connecticut

Affiliations

College of Liberal Arts and Sciences

Statistics

Nalini Ravishanker is currently serving as a College Of Liberal Arts And Sciences Professor at University of Connecticut.

Honors

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Experiences

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Education

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Nalini Ravishanker

University of Connecticut

Professor

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statistics data science time series applied mathematics transportation engineering statistical methodology forecasting applied statistics transportation research biostatistics transport safety bayesian analysis econometrics statistical inference safety traffic management road traffic safety financial time series analysis high-frequency financial econometrics finance research financial studies estimation theory statistical theory safety science traffic engineering frailty volatility modeling financial econometrics computational statistics travel research occupational safety safety analysis bayesian inference driver behavior transport economics unsignalized intersections system safety mobility public transport transport accident transport sector nhpp models economic modelling traffic psychology quantitative management customer profitability transportation planning transport infrastructure topological data analysis traffic prediction traffic theory nonlinear time series bayesian econometrics financial mathematics bayesian estimation stable innovations multivariate survival analysis safety effects vector arfima processes temporal modeling simultaneous prediction intervals gap acceptance left turns elderly drivers maximum likelihood estimation marketing science consumer research business research reliability engineering multichannel management buying behaviors technical safety automotive engineering multichannel shopping multichannel marketing resources two-lane highway segments statistical modeling systems engineering infrastructure engineering systems modeling statistical analysis time series modeling statistical methods modeling and simulation

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Bayesian Inference for Zero-Modified Power Series Regression Models. Mathematics (2024)

Katiane S. ConceiçãoMarinho G. AndradeVictor Hugo LachosNalini Ravishanker

bayesian statisticsstatistical methodologybayesian analysisstatistical theorymathematicsbayesian inferencestatistical inferenceapplied mathematicsstatistics

Hierarchical Modeling of Irregularly Spaced Financial Returns. Stat (2024)

Sreeram AnantharamanNalini RavishankerSumanta Basu

statistical methodologyfinancial time series analysisforecastinghigh-frequency financial econometricstime serieseconometricshamiltonian monte carlomonte carlo averagesfinancial econometricsvolatility modelingfinancebayesian analysisstatisticsstatistical models

Investigating the Joint Probability of High Coastal Sea Level and High Precipitation. Journal of Marine Science and Engineering (2024)

Namitha Viona PaisJames O’DonnellNalini Ravishanker

climate changebiostatisticsoceanographyearth sciencemeteorologymarine engineeringmarine meteorologyjoint probabilityhigh precipitationenvironmental sciencecoastal researchoceanic scienceclimatologyhydrology

Latent Level Correlation Modeling of Multivariate Discrete-Valued Financial Time Series. The Annals of Applied Statistics (2024)

Yanzhao WangHaitao LiuJian ZouNalini Ravishanker

latent modelingstatistical methodologyfinancial economicsfinance researchhigh-frequency financial econometricstime seriesfinancial studiesfinancial mathematicsfinancial econometricsfinancial time series analysismathematical financefinanceapplied mathematicsstatisticsstatistical models

Modeling Multivariate Positive‐Valued Time Series Using R‐Inla. Applied Stochastic Models in Business and Industry (2024)

Chiranjit DuttaNalini RavishankerSumanta Basu

statistical methodologyfinancial time series analysisforecastingfinance researchfinancial studiesvector autoregression evolutionstatisticsbayesian econometricsflexible levelfinancial econometricsvolatility modelingfinancebayesian analysistime seriesstatistical models

Multilevel Discrete Outcome Modeling for Crash Severity: A Novel Approach for Crash Severity Models. Transportation Research Record Journal of the Transportation Research Board (2024)

Md Julfiker HossainNamitha PaisJohn N. IvanShanshan ZhaoKai WangNalini Ravishanker

transportation researchcrash severitystatisticsmodeling and simulationtransportation engineeringcrash severity models

Randomized Feature Selection Based Semi-Supervised Latent Dirichlet Allocation for Microbiome Analysis. Scientific Reports (2024)

Namitha PaisNalini RavishankerSanguthevar RajasekaranGeorge WeinstockDong-Binh Tran

scientometricsmicrobiome analysisscientific computingsemi-supervised learningfeature selectiondata sciencescientific datahealth sciencesbioinformaticsmachine learning

Subsampling Algorithms for Irregularly Spaced Autoregressive Models. Algorithms (2024)

Jiaqi LiuZiyang WangHaiYing WangNalini Ravishanker

computer sciencesamplingalgorithmic developmentautoregressive models

Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series. Algorithms (2024)

Namitha PaisNalini RavishankerSanguthevar Rajasekaran

computer scienceclusteringcategorical time seriestime seriesobject categorizationautomatic classificationclassification methoddata sciencetopic modelsapplied mathematicsmachine learning

A Fast Privacy-Preserving Patient Record Linkage of Time Series Data. Scientific Reports (2023)

Ahmed SolimanSanguthevar RajasekaranPatrick TomanNalini Ravishanker

data preservationdata privacymachine learningtime series datadata sciencehealth sciencestime series

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Nalini Ravishanker

Professor

University of Connecticut

Affiliations

College of Liberal Arts and Sciences

Statistics

Nalini Ravishanker is currently serving as a College Of Liberal Arts And Sciences Professor at University of Connecticut.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Nalini Ravishanker

University of Connecticut

Professor

You can click a keyword to filter the displayed items

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Bayesian Inference for Zero-Modified Power Series Regression Models. Mathematics (2024)

Katiane S. ConceiçãoMarinho G. AndradeVictor Hugo LachosNalini Ravishanker

bayesian statisticsstatistical methodologybayesian analysisstatistical theorymathematicsbayesian inferencestatistical inferenceapplied mathematicsstatistics

Hierarchical Modeling of Irregularly Spaced Financial Returns. Stat (2024)

Sreeram AnantharamanNalini RavishankerSumanta Basu

Investigating the Joint Probability of High Coastal Sea Level and High Precipitation. Journal of Marine Science and Engineering (2024)

Namitha Viona PaisJames O’DonnellNalini Ravishanker

Latent Level Correlation Modeling of Multivariate Discrete-Valued Financial Time Series. The Annals of Applied Statistics (2024)

Yanzhao WangHaitao LiuJian ZouNalini Ravishanker

Modeling Multivariate Positive‐Valued Time Series Using R‐Inla. Applied Stochastic Models in Business and Industry (2024)

Chiranjit DuttaNalini RavishankerSumanta Basu

Multilevel Discrete Outcome Modeling for Crash Severity: A Novel Approach for Crash Severity Models. Transportation Research Record Journal of the Transportation Research Board (2024)

Md Julfiker HossainNamitha PaisJohn N. IvanShanshan ZhaoKai WangNalini Ravishanker

transportation researchcrash severitystatisticsmodeling and simulationtransportation engineeringcrash severity models

Randomized Feature Selection Based Semi-Supervised Latent Dirichlet Allocation for Microbiome Analysis. Scientific Reports (2024)

Namitha PaisNalini RavishankerSanguthevar RajasekaranGeorge WeinstockDong-Binh Tran

scientometricsmicrobiome analysisscientific computingsemi-supervised learningfeature selectiondata sciencescientific datahealth sciencesbioinformaticsmachine learning

Subsampling Algorithms for Irregularly Spaced Autoregressive Models. Algorithms (2024)

Jiaqi LiuZiyang WangHaiYing WangNalini Ravishanker

computer sciencesamplingalgorithmic developmentautoregressive models

Supervised Dynamic Correlated Topic Model for Classifying Categorical Time Series. Algorithms (2024)

Namitha PaisNalini RavishankerSanguthevar Rajasekaran

computer scienceclusteringcategorical time seriestime seriesobject categorizationautomatic classificationclassification methoddata sciencetopic modelsapplied mathematicsmachine learning

A Fast Privacy-Preserving Patient Record Linkage of Time Series Data. Scientific Reports (2023)

Ahmed SolimanSanguthevar RajasekaranPatrick TomanNalini Ravishanker

data preservationdata privacymachine learningtime series datadata sciencehealth sciencestime series

1
2
3
4
5
18
10 / page

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mobility

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Chris Tsokos

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reliability analysis

ecological modelling

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transportation engineering

systems engineering

Kun Xie

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machine learning research

Peter T. Savolainen

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infrastructure engineering

traffic engineering

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theoretical biology

biological computation