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In fMRI, the brain of a subject is scanned while he/she responds to some experimental stimuli. The brain is divided into voxels and for each voxel, there is a time series of measurements indicating the response of that part of the brain to the stimuli. A regression is typically performed for each voxel, with the regressor being some model representing the experimental stimuli.

In this project, Martin Lindquist and I are concerned with model checking and identifying model misfit. We do this by studying the residuals of the regression (as in typically done in other statistical applications). Specifically, we do this by using some sort of scan statistic to look for peaks in consecutive residuals. A Monte Carlo test can be done to check the significance of the scan statistic (and indicative of model misfit). We also looked at using Sidak's inequality as a non-computationally intensive method to obtain upper bounds on the p values.

We studied various types of model misfit and examined the power of such a test under these different types of misfit. We also worked out expressions for the bias of the regression estimates as well as how the regular regression t test is affected when the assumed predictor model is incorrect.

Other related projects include extending this to multiple independent runs and to the spatial setting involving multiple voxels.
This is a summary of my work in this project. For more details, you can go to http://web.njit.edu/~loh/Astro

Astronomers are interested in understanding the large scale structure that is evident in the universe. Various astrophysical theories have been developed to explain how such large scales of clustering could have developed.

In this project, we study the clustering properties of what are called absorption systems (or simply absorbers). These are believed to be non-luminous gas/dust clouds near very distant galaxies. They can be observed when they lie between the Earth and very bright quasi-stellar objects (~QSOs). By examining the spectra of light observed, the presence of these absorption systems, and their locations on the lines-of-sight to the ~QSOs can be determined. Understanding the clustering of absorption systems can (i) aid in understanding the true nature of absorption systems; (ii) serve as complements to studies of clustering of luminous matter.

An absorber catalog consists of the positions of absorbers on lines-of-sight. We developed a method to measure the degree of clustering of absorbers at various scales that includes information of absorber pairs from different lines-of-sight. It involves figuring out the correction factor needed to account for edge effects. Doing this can substantially reduce standard errors of estimates compared to more commonly used methods that obtain measures using only information of absorber pairs on the same line-of-sight.

This work has been published in Astrophysical Journal and the Journal of the American Statistical Association.

Further work involve (i) applying this method to the upcoming Sloan Digital Sky Survey (SDSS) absorber data. This dataset is 1000 times larger than the dataset we originally used; (ii) extending this method to estimate third-order moment characteristics. Doing this will allow us to examine the behavior of triplets of points. Current datasets are too small for this to be applicable, but the SDSS data will be large enough for third-order moment characteristics to be estimated. This can then be compared with similar measures of galaxies and stars.
The method of bootstrap is now a well-established method for statistic inference of independent data, so much so that it is often applied to dependent data (time series and spatial data).  Theoretical justification of the boostrap for dependent data is often complex and involves assuming some kind of mixing condition that limits the range of dependence. A lot of this work was done by Kunsch, Lahari and others.

My work in this area is twofold: (1) developing a new resampling scheme and (2) understanding bootstrap of Gaussian random fields under fixed-domain asymptotics.

1. We (Michael Stein and I) developed a modified resampling scheme, which we call the marked point method. Often, spatial bootstrap is done by block bootstrap, using windows to resample points. These windows/blocks are then joined together to form the new bootstrap sample. The use of windows is an attempt to capture the dependence present in the data (see Kunsch 1989). In the marked point method, we first assign to each point a mark. The sum of these marks give the statistic of interest. During the bootstrap procedure (and this can be done using blocks), the marks are resampled along with the points. By doing this the effect of joining independent blocks is reduced. 

This work has been applied to an astronomy dataset, and has been published in Astrophysical Journal and Statistica Sincia.

(Note: Kunsch in his 1989 paper, also introduced a block-of-blocks bootstrap that is very similar to the marked point bootstrap. It is, however, very rarely mentioned. This could be due to the complex notation. The formulation of the marked point bootstrap is very simple and makes it straightforward to implement. There is also additional flexibility with the marked point bootstrap, for example, in including corrections for edge effects.)

Further work include studying the behavior of this new method under various models of dependence; comparing with other bootstrap schemes, especially in the presence of edge effects; proving theoretical results.

2.  Most theoretical work done on the bootstrap of dependent data considers increasing domain asymptotics, i.e. the number of observations increase as the observation region increases. By assuming some sort of mixing condition together with an increasing domain, the blocks become almost independent. For random fields, it is not unusual to have a fixed observation region, and increasing number of observations in the region. An example is observations of the Cosmic Microwave Background (CMB). The (spherical) region is fixed, but as technology improves data at a finer and finer resolution becomes possible.

We cannot expect general theoretical results for the bootstrap in fixed domain asymptotics. This is because the dependence in the data does not decrease as the number of data point increase. We consider instead certain specific models for the correlation structure of a Gaussian random field in one dimension and show that the bootstrap can be consistent for some parameters when we difference the data to reduce the amount of dependence. We find that the amount of differencing needed depends on the smoothness of the random field. 

Results of this work will appear in Statistica Sinica.

Further work involve considering Gaussian random fields in two dimensions.
If you are thinking of statistics as a major or joint major, here are some links about careers in statistics that might be useful:
** [[American Statistical Association|http://www.amstat.org/careers]] has information about careers in statistics. There are brochures that you can download. There is also a pdf file giving statistics about salaries.
** [[Royal Statistical Society|http://www.rss.org.uk/main.asp?page=1999]] also has information about careers in statistcs. There is a powerpoint presentation that you can download. The RSS is based in the UK, so some information may not be relevant.
** Do an internet search for e.g. "statistics career" and you will probably find many other useful sites. Some of these are maintained by professors in other universities.
Here are links to some of my collaborators' webpages:

[[Regina Dolgoarshinnykh|http://www.stat.columbia.edu/~regina/]]
[[Tamraparni Dasu|www.research.att.com/people/Dasu_Tamraparni/]]
[[Yongtao Guan|http://www.bus.miami.edu/faculty-and-research/faculty-directory/management-science/guan/]]
[[Woncheol Jang|http://jang.myweb.uga.edu/]]
[[Naa Oyo Kwate|http://www.rci.rutgers.edu/~nokwate/]]
[[Martin Lindquist|http://www.biostat.jhsph.edu/~mlindqui/]]
[[Malgosia Madajewicz|http://iserp.columbia.edu/node/172]]
[[Jean Quashnock|http://faculty.carthage.edu/jquashnock/]]
[[Michael Stein|http://www.stat.uchicago.edu/faculty/stein.shtml]]
[[Ryan Yue|http://zicklin.baruch.cuny.edu/faculty/profiles/yu-ryan-yue]]
[[Zhengyuan Zhu|http://www.public.iastate.edu/~zhuz/]]

[[Welcome]]
[[My Contact]]
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This is not an exhaustive list, just what I found useful to know. I spent a bit of time summarizing this information and I am putting it here so I can find it again.

Arxiv.org - all IMS and Bernoulli Society(?) journals (some delay for current issue)

IMS membership ($95, or $76 if renew early) gives full electronic access to all IMS journals including
Annals of applied statistics (AOAS)
Annals of Statistics
Statistical Science

JASA - free with ASA membership $125
American Statistician - free with ASA membership
Journal of Agricultural and Environmental Statistics (JABES) $50
Journal of Computation and Graphical Statistics (JCGS) $65
Technometrics $30

JRSS B and C (JRSS) - one free with membership 82 pounds, 40 pounds each extra
Statistica Sinica (ICSA) - free with ICSA membership $40

Scandinavian Journal of Statistics ($58 with ASA/IMS membership, else $68)
Biometrika $68
Biometrics - free with International Biometric Society membership $60

Too expensive if not from institutional subscription!
Environmetrics
Journal of Statistical Planning and Inference (JSPI)

Bayesian Analysis - open access 
[[SentiWeb|http://www.sentiweb.org]]: Monitoring of disease incidence in France
[[Stock data|http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html]] on Kenneth French's webpage

[[Sloan Digital Sky Survey|http://www.sdss.org]]
[[Center for AstroStatistics|http://astrostatistics.psu.edu/]] at Penn State
[[AT&T Labs Research|http://research.att.com]]
[[My Contact]]
[[CV|http://web.njit.edu/~loh/HomeFiles/jmcv.pdf]]
[[Research]]
{{indent{[[Papers]]
{{indent{[[Talks]]
{{indent{[[Astronomy]]
[[Statistical Consulting]]
[[Teaching]]
[[Resources]]
[[Links]]



Dept of Mathematical Sciences
New Jersey Institute of Technology
Newark, NJ 07102

Tel:   973-596-2949
Fax:   973-596-5591
Email: loh at njit dot edu
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# Berti, Loh and Thirumuruganathan - Are outlier detection methods resilient to sampling? (under preparation)
#[[Cheng and Loh|http://web.njit.edu:/~loh/Papers/SentimentAnalysis07.pdf]] - Learning-based method with valence shifters for sentiment analysis - 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (submitted).
# [[Loh and Jang (2017)|http://web.njit.edu/~loh/Papers/amise-05.pdf]] - Bandwidth Selection for Estimating the ~Two-Point Correlation Function of a Spatial Point Pattern Using AMSE - Statistica Sinica (to appear).
#[[Jang and Loh (2017)|http://web.njit.edu/~loh/Papers/ApJ.LohJang.2013.pdf]] - Quantitative Comparison of Two-point Correlation Functions from Real and Mock SDSS Galaxy Surveys - Astrophysical Journal, 839.
# [[Fang and Loh (2017)|http://web.njit.edu/~loh/Papers/SIM-2015.pdf]] - Single-index model for inhomogeneous spatial point processes - Statistica Sinica, 27, 555-574.
#[[Artigas, Loh, Shin, Grzyb and Yao (2017)|http://web.njit.edu/~loh/Papers/CreekSedimentsSandy2017.pdf]] - Baseline and distribution of organic pollutants and heavy metals in tidal creek sediments after Hurricane Sandy in the Meadowlands of New Jersey - Environmental Earth Sciences, 76, 293.
#[[Ihde, Loh and Rosen|http://web.njit.edu/~loh/Papers/CordBloodStudy.pdf]] - Association of environmental chemicals and estrogen metabolites in children - BMC Endocrine Disorders (submitted).
# [[Kwate and Loh (2016)|http://web.njit.edu/~loh/Papers/ChicagoVice-draft.pdf]] - Fast food and liquor store density, co-tenancy, and turnover: Vice store operations in Chicago, 1995-2008 - Applied Geography, 67, 1-13.
# [[Ihde, Boscamp, Loh, Rosen (2015)|http://web.njit.edu/~loh/Papers/HUMC-headlice2015.pdf]] -  Safety and efficacy of a 100% dimethicone pediculocide in school-age children - BMC Pediatrics, 15:70.
# [[Yue and Loh (2015)|http://web.njit.edu/~loh/Papers/CJS.YueLoh.2015.pdf]] - Variable Selection for Inhomogeneous Spatial Point Processes - Canadian Journal of Statistics, 43, 288-305.
# [[Ihde et al (2014)|http://web.njit.edu/~loh/Papers/HUMC-autism2014.pdf]] - Mapping contaminants associated with autism: a public health pilot in New Jersey - Journal of Geographic Information System, 6, 706-722.
# [[Jang, Lim, Lazar, Loh and Yu (2014)|http://web.njit.edu/~loh/Papers/JKSS.Jang.etal.2014.pdf]] - Some Properties of Generalized Fused Lasso and Its Applications to High Dimensional Data - Journal of the Korean Statistical Society (published online).
# [[Dasu, Loh and Srivastava (2014)|http://web.njit.edu/~loh/Papers/disguised_submitted.pdf]] - Empirical Glitch Explanations - 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).
# [[Loh (2014)|http://web.njit.edu/~loh/Papers/SADM.Loh.2013.pdf]] - Spatial detection of anomalous cellular network events - Statistical Analysis and Data Mining, 7, 212-225.
# [[Moore, Bain, Loh and Levison (2014)|http://web.njit.edu/~loh/Papers/PDGF-ASNNeuro.pdf]] - ~PDGF-responsive progenitors persist in the subventricular zone across the lifespan - ASN Neuro, 6, 65-81.
# [[Berti, Loh and Dasu (2013)|http://web.njit.edu/~loh/Papers/icdm2013.lohetal.pdf]] - A Masking Index for Quantifying Hidden Glitches - IEEE 13th International Conference on Data Mining (ICDM), doi:10.1109/ICDM.2013.16.
# [[Loh (2013)|http://web.njit.edu/~loh/Papers/SII.Loh.2013.pdf]] - Comparing spatial densities characterizing human mobility using ratio maps - Statistics and Its Interface, 6, 577-584.
# [[Yue and Loh (2013)|http://web.njit.edu/~loh/Papers/JNPS.YueLoh.2013.pdf]] - Bayesian nonparametric estimation of pair correlation function for inhomogeneous spatial point processes - Journal of Nonparametric Statistics, 25, 463-474.
# [[Becker, Caceres, Hanson, Isaacman, Loh, Martonosi, Rowland, Urbanek, Varshavsky and Volinksy (2013)|http://web.njit.edu/~loh/Papers/CACM.Becker.etal.2013.pdf]] - Human mobility characterization from cellular network data - Communications of the ACM, 56, 74-82.
# [[Loh and Dasu (2012)|http://web.njit.edu/~loh/Papers/ICDM.LohDasu.2012.pdf]] - Effect of data repair on mining network streams - International Conference on Data Mining (ICDM), Data Mining in Networks Workshop.
# [[Loh and Dasu (2012)|http://web.njit.edu/~loh/Papers/IJIQ.LohDasu.2012.pdf]] - Auditing Data Streams for Correlated Glitches - International Journal of Information Quality, 3(2), 85-106.
# [[Dasu and Loh (2012)|http://web.njit.edu/~loh/Papers/VLDB.DasuLoh.2012.pdf]] - Statistical distortion: consequences of data cleaning - 38th International Conference on Very Large Data Bases (VLDB), 5(11), 1674-1683.
# [[Kwate, Loh, White and Saldana (2012)|http://web.njit.edu/~loh/Papers/JUrbanHealth.Kwate.etal.2012.pdf]] - Retail redlining in New York City: Racialized access to day-to-day retail resources - Journal of Urban Health, DOI:10.1007/s11524-012-9725-3.
# [[Yue, Lindquist and Loh (2012)|http://web.njit.edu/~loh/Papers/AOAS.Yue.etal.2012.pdf]] - Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression - Annals of Applied Statistics, 6, 697-718.
# [[Yau and Loh (2012)|http://web.njit.edu/~loh/Papers/Sinica.YauLoh.2011.pdf]] - A Generalization of the ~Neyman-Scott Process - Statistica Sinica, 22, 1717-1736.
# [[Loh and Dasu (2011)|http://web.njit.edu/~loh/Papers/ICIQ.LohDasu.2011.pdf]] - Auditing Data Streams for Correlated Glitches - International Conference of Information Quality 2011.
# [[Becker, Caceres, Hanson, Loh, Urbanek, Varshavsky and Volinsky (2011)|http://web.njit.edu/~loh/Papers/UbiComp.Becker.etal.2011.pdf]] - Route classification using cellular handoff patterns - 13th ACM International Conference on Ubiquitous Computing (~UbiComp) 2011 (also at ~NetMob 2011).
# [[Becker, Caceres, Hanson, Loh, Urbanek, Varshavsky and Volinsky (2011)|http://web.njit.edu/~loh/Papers/PURBA.Becker.etal.2011.pdf]] - Clustering anonymized mobile call detail records to find usage groups - 1st Workshop on Pervasive Urban Applications (PURBA) 2011 (also at ~NetMob 2011).
# [[Becker, Caceres, Hanson, Loh, Urbanek, Varshavsky and Volinsky (2011)|http://web.njit.edu/~loh/Papers/IEEEPervasive.Becker.etal.2011.pdf]] - A tale of one city: using cellular network data for urban planning - IEEE Pervasive Computing, 10, 18-26 (also at ~NetMob 2011).
# [[Yue and Loh (2011)|http://web.njit.edu/~loh/Papers/Biometrics.YueLoh.2011.pdf]] - Bayesian Nonparametric Intensity Estimation for Inhomogeneous Spatial Point processes - Biometrics, 67, 937-946.
# [[Loh (2011)|http://web.njit.edu/~loh/Papers/Environmetrics.Loh.2011.pdf]] - K-scan for Anomaly Detection in Disease Surveillance - Environmetrics, 22, 179-191.
# [[Hariharan, Loh, Shanahan and Yamada (2010)|http://dl.acm.org/citation.cfm?id=1869863]] - Spatial Probabilistic Modeling of Calls to Businesses - ACM SIGSPATIAL 2010.
# [[Kwate and Loh (2010)|http://www.sciencedirect.com/science/article/pii/S0091743510001751]] - Separate and Unequal: The Influence of Neighborhood and School Characteristics on Spatial Proximity between Fast Foods and Schools - Preventive Medicine, 51, 153-156.
# [[Loh and Jang (2010)|http://web.njit.edu/~loh/Papers/JRSSC.LohJang.2010.pdf]]  - Estimating a Cosmological Mass Bias Parameter with ~Semi-Parametric Bootstrap Bandwidth Selection - Journal of the Royal Statistical Society, Series C, 59, 761-779.
# [[Yue, Loh and Lindquist (2010)|http://web.njit.edu/~loh/Papers/SII.Yue.etal.2010.pdf]] - Adaptive Spatial Smoothing of fMRI images - Statistics and Its Interface, 3, 3-13.
# [[Jang and Loh (2010)|http://web.njit.edu/~loh/Papers/AOAS.JangLoh.2010.pdf]] - Density Estimation for Grouped Data with Application to Line Transect Sampling - Annals of Applied Statistics, 4, 893-915.
# [[Loh (2010)|http://web.njit.edu/~loh/Papers/JSPI.Loh.2010.pdf]] - Bootstrapping an Inhomogeneous Point Process - Journal of Statistical Planning and Inference, 140, 734-749.
# [[Kwate, Yip, Loh and Williams (2009)|http://web.njit.edu/~loh/Papers/HealthPlace.Kwate.etal.2009.pdf]] - Inequality in Obesigenic Environments: Fast Food Density in New York City - Health and Place, 15, 364-373
# [[Lindquist, Loh, Atlas and Wager (2008)| http://dx.doi.org/10.1016/j.neuroimage.2008.10.065]] - Modeling the Hemodynamic Response Function in fMRI: Efficiency, Bias and Mis-modeling - ~NeuroImage, 45, ~S187-S198.
# [[Loh, Lindquist and Wager (2008)|http://web.njit.edu/~loh/Papers/Sinica.Loh.etal.2008.pdf]] - Residual Analysis for Detecting Mismodeling in fMRI Images - Statistica Sinica, 18, 1421-1448.
# [[Loh (2008)|http://web.njit.edu/~loh/Papers/ApJ.Loh.2008a.pdf]] - A Valid and Fast Spatial Bootstrap for Correlation Functions -  Astrophysical Journal, 681, 726-734.
#[[Loh (2008)|http://web.njit.edu/~loh/Papers/ApJ.Loh.2008.pdf]] - Estimating ~Third-Order Moments for an Absorber Catalog - Astrophysical Journal, 674, 636-643.
# [[Loh and Stein (2008)|http://www3.stat.sinica.edu.tw/statistica/J18N2/J18N214/J18N214.html]] - Spatial Bootstrap with Increasing Observations in a Fixed Domain - Statistica Sinica, 18, 667-688
# [[Guan and Loh (2007)|http://web.njit.edu/~loh/Papers/JASA.GuanLoh.2007.pdf]] - A thinned block Bootstrap Procedure for Modeling Inhomegeneous Spatial Point Patterns - Journal of the American Statistical Association, 102, 1377-1386.
# [[Loh and Zhu (2007)|http://web.njit.edu/~loh/Papers/AOAS.LohZhu.2007.pdf]] - Accounting for Spatial Correlation in the Scan Statistic - Annals of Applied Statistics, 1, 560-584.
# [[Rzhetsky, Iossifov, Loh and White (2006)|http://web.njit.edu/~loh/Papers/PNAS.Rzhetsky.etal.2006.pdf]] - Microparadigms: Chains of Collective Reasoning in Publications about Molecular Interactions - Proceedings of the National Academy of Science, 103, p4930-4945.
# [[Loh and Stein (2004)|http://web.njit.edu/~loh/Papers/Sinica.LohStein.2004.pdf]] - Bootstrapping a Spatial Point Process - Statistica Sinica, 14, p69-101.
# [[Loh, Stein and Quashnock (2003)|http://web.njit.edu/~loh/Papers/JASA.Loh.etal.2003.pdf]] - Estimating the ~Large-Scale Structure of the Universe using ~Quasi-Stellar Object Carbon IV Absorbers - Journal of the American Statistical Association, 98, p522-532.
# [[Loh, Quashnock and Stein (2001)|http://web.njit.edu/~loh/Papers/ApJ.Loh.etal.2001.pdf]] - A Measurement of the ~Three-Dimensional Clustering of C IV ~Absorption-Line Systems on Scales of 5-1000 h^^-1^^ Mpc - Astrophysical Journal, 560, p606-612.
# [[Stein, Quashnock and Loh (2000)|http://web.njit.edu/~loh/Papers/Annals.Stein.etal.2000.pdf]] - Estimating the ~K-Function of a Point Process with an Application to Cosmology - Annals of Statistics, 28, p1508-1532.
These pointers I collected from a variety of sources (articles, books etc). They are not rules, merely guidelines. They are not in any particular order. I find it helpful to go through these every now and then, because I tend to forget!

# Make sure fonts and figures are not too small; do not put too many lines of text.
# Don't show scans of your papers.
# Don't use red or green - people who are color-blind can't see these colors.
# Don't put too many equations on a page; your talk shouldn't focus too much on the technical details.
# If you show a figure, describe it clearly and in detail.
# Provide essential background and definitions.
# Avoid unnecessarily fanciful transitions, clip art ...
# Speak clearly, loud enough, and not too quickly.
# Try not to cram too much into a single talk. A commonly expressed guideline is 1 slide for every 2 minutes (although I have heard excellent talks that break this "rule"). Have one (good) main idea and develop it.
# The first few slides (first 5 minutes?) are important - this is where you gain or lose the interest of your audience. Start strong: be enthusiastic; describe an interesting motivating example ...
# use sans serif fonts (serif fonts have connecting strokes that make characters flow together - they may be good for books, but make words difficult to read on the screen.)
# Some people tend to let their voice drop off towards the end of a sentence. Makes it difficult to hear what they are saying.
# Practice - vocalizing your ideas/thoughts helps to clarify your thinking and to keep your talk running smoothly. You also get an idea of the length of your talk.
# Be organized - if you will be referring to a slide often, set it aside after use (for transparencies), or have copies of that slide at appropriate locations in your presentation so that you don't have to scroll through your Powerpoint presentation looking for that one slide..
# Do a quick run-through of your Powerpoint presentation e.g. to make sure that sounds/movies work.
# Keep copies of files in various places - e.g. in your laptop, in a couple of usb drives, on a server.
# If you are using an overhead transparency projector, be aware of where you are standing and whether your arms or shoulders are blocking the light.
# End on time - don't expect others to be as interested in your work as you are.
My main area of interest is in spatial statistics. A main feature in spatial statistics is dependence or correlation between data points. The two main areas of spatial statistics are geostatistics and spatial point patterns.

In geostatistics, the data usually consists of observations within a region of a continuous random field, for example, measurements of ozone at monitoring stations. There is underlying variability in the observations at any one location, but more importantly, there is correlation between the observations at different locations. We can model the dependence structure and use the fitted model to make predictions at other locations. Sometimes the interest is in modeling the dependence of the observations on some measured covariates.

In spatial point patterns, the data consists of observations of objects in a region, e.g. positions of galaxies in space, of trees in a forest. Descriptive statistics would include the intensity of points and measure of clustering or inhibition of points. Various models, such as Gibbs model, log-Gaussian Cox processes, the ~Neymann-Scot model, can be used to model the observed point pattern. 

Some of my work is supported by NSF grants. Specifically, NSF #~AST-0507687 was awarded for work in astrostatistics. NSF #~SES-0624256 was awarded for work in Human and Social Dynamics.

__Description of some projects__:
[[Data Quality]]
[[Astronomy]]
[[Bootstrap of Spatial Data]]
[[Scan statistic for correlated data]]
[[Analysis of fMRI data]]

[[Papers]]
[[Collaborators]]
[[Statistical Computing Resources|http://www.ats.ucla.edu/stat/]] at UCLA (with resources to learn R, SAS, Stata and SPSS)

[[JSTOR|http://www.jstor.org]]
[[Arxiv.org|http://www.arxiv.org]]
[[Project Euclid|http://projecteuclid.org]]
[[Numerical Recipes|http://www.nr.com]]

Information about subscription rates for statistics [[Journals]], which ones you get with memberships etc (correct as of Oct 2009)

Giving a talk? Robert Geroch has some [[suggestions|http://arxiv.org/abs/gr-qc/9703019]]. 

!!! The R statistical package
Official R [[website|http://www.r-project.org]]
[[Link|http://cran.r-project.org/manuals.html]] to R manuals
R [[reference card|http://web.njit.edu/~loh/Reference/Rrefcard.pdf]] containing 4 pages of useful commands.
[[Statsci.org|http://www.statsci.org/r.html]] webpage on R
[[Webpage|http://www.stats.ox.ac.uk/pub/MASS3/]] for the 3rd edition of the Modern Applied Statistics with S book by Venables and Ripley. The online [[complements|http://www.stats.ox.ac.uk/pub/MASS3/Compl.shtml]] has a section for using the book with R.

__Some R tutorials on the web__:
#[[Resources to help you learn and use R|http://www.ats.ucla.edu/stat/r/]]
#[[Using R|http://mercury.bio.uaf.edu/mercury/R/R.html]]
#[[R tutorial at Union College|http://www.cyclismo.org/tutorial/R]]
#[[R tutorial at Illinois State U|http://www.math.ilstu.edu/dhkim/Rstuff/Rtutor.html]]
#[[Statistics with R|http://zoonek2.free.fr/UNIX/48_R/all.html]]
#[[R intro|http://www.stat.berkeley.edu/~spector/R.pdf]]
#[[Econometrics in R|http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf]]


The scan statistic was studied by Naus (1965) as a method to identify clusters in a one-dimensional point process. Kulldorff (1997) extended it to the spatial setting, so that clusters in a multidimensional point process can be identified. The Spatial Scan Statistic is widely used in epidemiology. The spatial scan statistic can be computed using the [[SatScan|http://www.satscan.org]] software.

An underlying assumption of the method is the independence between points.  In certain cases, such as the occurrence of an infectious disease, some positive correlation between the occurrence of points is expected. Furthermore, in the Poisson model studied by Kulldorff (1997), there could be overdispersion during to unmeasured covariates or measurement error.

In a simulation study, we find that when there is positive correlation or overdispersion, there is an increased chance in finding significant clusters, resulting in more false alarms.

We developed a method to reduce the number of false alarms. This involves modeling any dependence found in the data using a spatial generalized linear model (Diggle and Tawn 1998). The fitted model is then used in the Monte Carlo step employed to obtain the p value of an identified cluster.
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I am the coordinator of the Statistical Consulting Laboratory. The Statistical Consulting Lab aims to offer high quality statistical consulting for the purposes of promoting research, collaboration and statistical education. The official webpage is here: [[SCL|http://math.njit.edu/research/resources/consulting-lab.php]]

We welcome requests from faculty and researchers from NJIT and other research institutions for consulting on statistical issues for grants and other research projects. In particular, we would like to encourage collaborative research between our statistics faculty and researchers from other disciplines to make significant advances in those disciplines. 

We can also provide help to NJIT graduate students with statistical issues related to their dissertation work, especially if the work involved is beneficial to our own Statistics ~PhD students.

A graduate student is often assigned to a consulting project under the supervision of faculty. Working on consulting projects serves to provide our students experience with analyzing real data as well as interacting and communicating effectively with clients. We believe that this is an important component of their training to become statisticians.

Our on-going external clients have included Beth Israel Hospital, New Jersey Meadowlands Commission and Hackensack University Medical Center.

Services provided include:
# Writing statistical methods for grant applications
# Advice on statistical analysis
# Statistical analysis of data
# Use of statistical software (R, SAS)
# Interpretation of results
# Choosing statistical methods, research designs, sample size calculations

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Joint Statistical Meeting, 2014: A masking index for quantifying hidden glitches
Institute of Mathematical Statistics Asia Pacific Rim Meeting 2014

National Institute of Education, Aug 2013: [[I have in my possession two maps ...|http://web.njit.edu/~loh/Talks/nie.20130823.pdf]]
Astrostatistics Summer School in Penn State University, Jul 2013: [[Lecture on spatial statistics|http://web.njit.edu/~loh/Talks/spatstat-20130607.pdf]]
Spatial Statistics Conference, Jul 2013: [[Bayesian estimation of the intensity of inhomogeneous point patterns|http://web.njit.edu/~loh/Talks/spatstatconf-20130606.pdf]]

International Conference on Data Mining Workshop, Dec 2012: Effect of data repair on mining network streams
38th Very Large ~DataBases conference (VLDB), Aug 2012: Statistical distortion: consequences of data cleaning (presented by Parni Dasu)
NJIT poster session, Sep 2012
Seminar at Baruch College, Nov 2012: [[K-scan for anomaly detection in spatial point patterns|http://web.njit.edu/~loh/Talks/baruch-20121116.pdf]]
University of Georgia, Apr 2012

NJIT 2011
16th International Conference on Information Quality, Nov 2011
3rd ~IMS-China International Conference on Statistics and Probability, Jul 2011
ICSA Applied Statistics Symposium, Jun 2011
Eastern North American Region (ENAR) Meeting, Mar 2011

NJIT 2010
JSM 2010
IWAP 2010

JSM 2009

FACM 2008

JSM 2007: [[Clustering of Absorptions Systems|http://web.njit.edu/~loh/Talks/jsm2007_loh.pdf]]
If you are enrolled in any of the courses that I am teaching, you can go to NJIT's [[Moodle|http://moodle.njit.edu]] site to access course files.

__Spring 2017__
Math 664 - Methods for Statistical Consulting
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Spring2017/Math_664-SP17.pdf]]

Math 665 - Methods for Statistical Consulting
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Spring2017/Math_665-SP17.pdf]]

__Fall 2016__
Math 244 - Introduction to Probability Theory
[[Course description|http://catalog.njit.edu/undergraduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Undergraduate/Course_Syllabi/Fall2016/Math_244-F16.pdf]]

Math 662 - Probability Distributions
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Fall2016/Math_662-F16.pdf]]

__Spring 2016__
Math 664 - Methods for Statistical Consulting
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Spring2016/Math_664-SP16.pdf]]

Math 661 - Applied Statistics
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Spring2016/Math_661-SP16.pdf]]

__Fall 2015__
Math 244 - Introduction to Probability Theory
[[Course description|http://catalog.njit.edu/undergraduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Undergraduate/Course_Syllabi/Fall2015/Math_244-F15.pdf]]

Math 662 - Probability Distributions
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Fall2015/Math_662-F15.pdf]]

__Spring 2015__
Math 664 - Methods for Statistical Consulting
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Spring2015/Math_664-SP15.pdf]]

Math 661 - Applied Statistics
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Spring2015/Math_661-SP15.pdf]]

__Fall 2014__
Math 661 - Applied Statistics
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Fall2014/Math_661-F14.pdf]]

Math 662 - Probability Distributions
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Fall2014/Math_662-F14.pdf]]

__Spring 2014__
Math 664 - Methods for Statistical Consulting
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Spring2014/Math_664-S14.html]]

__Fall 2013__
Math 662 - Probability Distributions 
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Fall2013/Math_662-F13.html]]

__Spring 2013__
Math 665 - Statistical Inference 
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Spring2013/Math_665-S13.html]]

__Fall 2012__
Math 662 - Probability Distributions 
[[Course description|http://catalog.njit.edu/graduate/science-liberal-arts/mathematical-sciences/#coursestext]]
[[Syllabus|http://m.njit.edu/Graduate/Course_Syllabi/Fall2012/Math_662-F12.html]]

[img[http://web.njit.edu/~loh/HomeFiles/jm2.jpg]]
@@display:block;text-align:left;border:0px solid #f00;margin-left:14em;margin-top:-10em;Hi, welcome to my homepage. 

I am an Associate Professor at the [[Dept of Mathematical Sciences|http://math.njit.edu]] in [[NJIT|http://www.njit.edu]]. My main research interest is in spatial statistics. I am also the coordinator of the [[Statistical Consulting Laboratory|http://math.njit.edu/research/resources/consulting-lab.php]].

Use the links on the left to navigate through the various sections of this webpage.