Profiles

Ola Hössjer

Ola G H Hössjer

Professor

Visa sidan på svenska
Works at Department of Mathematics (incl. Math. Statistics)
Telephone 08-16 45 84
Email ola@math.su.se
Visiting address Roslagsvägen 101, Kräftriket, hus 6
Room 318
Postal address Matematiska institutionen 106 91 Stockholm

About me

Welcome!

My name is Ola Hössjer and I'm Professor of Mathematical Statistics at Stockholm University.

I have two daughters, Evelina and Linnea, and live in Sollentuna north of Stockholm.

Teaching

Fall 2016, 2017: Linear statistical models (in Swedish)

Spring 2014, 2015, 2016, 2017, and Fall 2017: Categorical data analysis (in English)

Fall 2014: Coalescence theory and population genetics (in English)

Fall 2008: Stochastic processes III  (in English) 

Spring 2008: Population genetics and gene mapping (in English)

Fall 2007: Graduate course in probability theory  (in English)

Spring 2007: Stochastic processes and simulation I (in Swedish)

Fall 2006: Stochastic processes and simulation II (in English)

Spring 2006 and Fall 2007: Probability theory III (in Swedish 2006, English 2007)

Fall 2005 : Stochastic methods of population genetics  (in English)

Research

Over the years, my reserach has focused on different aspects of empirical processes, fractal measures, robust statistics, non-parametric methods, spatial statistics, biostatistics, statistical genetics, population genetics, insurance mathematics and signal processing. I like to work in the intersection between inference theory, probability theory and various applications of statistics.

Right now, one of my main areas of interest is population genetics, in particular computing the effective size of various populations, using mathematical tools such as coalescence theory and quasi equilibrium. This has implications for conservation biology, and I am cooperating with population geneticists on these topics.

I have always liked statistics as the bridge between deductive science (mathematics and logic) and inductive experimental science. For me, much of the beauty of mathematics is revealed when phenomena in different fields of applications are modeled in a succinct and hopefully general way.

Publications

Here is a list of my publications in reverse-chronological order. In the right column you find pdf files of most of these papers, sorted according to subject areas.

85. Hössjer, O., Alfredsson, L., Hedström, A.-K., Kockum, I., Lekman, M. and Olsson, T. (2017). Quantifying and estimating additive measures of interaction from case-control data. To appear in Modern Stochastics: Theory and Applications.

84. Hedström, A.-K., Katsoulis, M., Hössjer, O., Bomfim, I.L., Oturai, A., Sondergaard H.B., Selleberg, F., Ullum, H., Torner, L.W., Gustavsen, M.W., Harbo, H.F., Obradovic, D., Gianfrancesco, M.A., Barcellos, L.F., Schaefer, C.A., Hillert, J., Kockum, I., Olsson, T. and Alfredsson, L. (2017). The interaction between smoking and HLA genes in multiple sclerosis; replication and refinement. To appear in European Journal of Epidemiology.

83. Olsson, F., Laikre, L., Hössjer, O. and Ryman, N. (2017). GESP: A computer program for modeling genetic effective population size, inbreeding, and divergence in structured populations. Available online at Molecular Ecology Resources.

82. Hössjer, O., Laikre, L. and Ryman, N. (2016). Effective size and time to migration-drift equlibrium in geographically subdivided populations. Theoretical Population Biology 112, 139-156.

81. Hössjer, O., Kockum, I., Alfredsson, L., Hedström, A.-K., Olsson, T. and Lekman, L. (2017). A new normalization and generalized definition of attributable proportion. Available online at Epidemiologic Methods.

80. Laikre, L., Olsson, F., Jansson, E., Hössjer, O. and Ryman, N. (2016). Metapopulation effective size and conservation genetic goals for the Fennoscandic wolf population. Heredity 117, 279-289

79. Hössjer, O. and Tyvand, P. (2016). A monoecious and diploid Moran model of random mating. Journal of Theoretical Biology 394, 182-196. 

78. Hössjer, O., Tyvand, P.A. and Miloh, T. (2016). Exact Markov chain and approximate diffusion solution for haploid genetic drift with one-way mutation. Mathematical Biosciences 272, 100-112. 

77. Lekman, M., Karlsson, R., Graae, L., Hössjer, O., Paddock, S. and Kockum, I. (2015). A significant risk locus on 19q13 for bipolar disorder identified using a combined genome-wide linkage and copy number variation analysis. BioData Mining 8:42. (18 pages)

76. Olsson, F. and Hössjer, O. (2015). Equilibrium distributions and simulation methods for age structured populations. Mathematical Biosciences 268, 45-51.

75. Ekheden, E. and Hössjer, O. (2015). Multivariate time series modeling, estimation and prediction of mortalities. Insurance, Mathematics and Economics 165, 156-171.

74. Olsson, F. and Hössjer, O. (2015). Estimation of the effective population size in age structured populations. Theoretical Population Biology 101, 9-23.

73. Hössjer, O., Olsson, F., Laikre, L. and Ryman, N. (2015). Metapopulation inbreeding dynamics, effective size and subpopulation differentiation - a general analytical approach for  diploid organisms. Theoretical Population Biology 102, 40-59.

72. Lekman, M., Hössjer, O., Andrew, P., Källberg, H., Uvehag, D., Charney, D., Manji, H., Rush, J., McMahon, F., Moore J. and Kockum, I. (2014). The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder: An explorative study Biodata Mining 7:19. (18 pages)

71. Hössjer, O., Olsson, F., Laikre, L. and Ryman, N. (2014). A new general analytical approach for modeling patterns of genetic differentiation and effective size of subdivided populations over time. Mathematical Biosciences 258, 113-133.

70. Hössjer, O. (2015). On the eigenvalue effective size in structured populations. Journal of Mathematical Biology 73(3), 595-646.

69. Malmberg, H. and Hössjer, O. (2014). Probabilistic choice with an infinite set of options - an approach based on random sup measures. Chapter 18 in Modern Problems in Insurance Mathematics, D. Silvestrov and A. Martin-L\öf (eds), EEA Series, Springer International Publishing, Cham, Switzerland, pp.\ 291-312.

68. Ekheden, E. and Hössjer, O. (2014). Analysis of the stochasticity of mortality using variance decomposition. Chapter 13 in Modern Problems in Insurance Mathematics, D. Silvestrov and A. Martin-Löf (eds), EEA Series, Springer International Publishing, Cham, Switzerland, pp.\ 199-222.

67. Hössjer, O. and Ryman, N. (2014). Quasi equilibrium, variance effective population size and fixation index for models with spatial structure. Journal of Mathematical Biology 69(5), 1057-1128.

66. Olsson, F.  Hössjer, O., Laikre, L. and Ryman, N. (2013). Characteristics of the variance effective population size over time using an age structured model with variable size. Theoretical Population Biology 90, 91-103.

65. Ryman, N., Allendorf, F.W., Jorde P.E., Laikre, L. and Hössjer, O. (2013). Samples from subdivided populations yield biased estimates of effective size that overestimate the rate of loss of genetic variation. Molecular Ecology Resources 14, 87-99.

64. Hössjer, O. (2013). Spatial autocorrelation for subdivided populations with invariant migration schemes. Methodology and Computing in Applied Probability 16(4), 777-810.

63. Hössjer, O., Jorde, P.E. and Ryman, N. (2013). Quasi equilibrium approximations of the fixation index under neutrality: The island model. Theoretical Population Biology 84, 9-24.

62. Ekheden, E. and Hössjer, O. (2012). Pricing catastrophe risk in life (re)insurance. Scandinavian Actuarial Journal 2012, 1-16.

61. Verrall, R., Hössjer, O. and Björkwall, S. (2012). Modelling claims run-off with reversible jump Markov Chain Monte Carlo methods. ASTIN Bulletin 42(1), 35-58.

60. Hössjer, O. (2011). Coalescence theory for a general class of structured populations with fast migration. Advances in Probability Theory 43(4), 1027-1047.

59. Grünewald, M. and Hössjer, O. (2011). A general statistical framework for multistage designs. Scandinavian Journal of Statistics 39(1), 131-152.

58. Björkwall, S., Hössjer, O., Ohlsson, E. and Verrall, R. (2011). A generalized linear model with smoothing effects for claims reserving. Insurance, Mathematics and Economics 49, 27-37.

57. Grünewald, M., Hössjer, O. and Humphreys, K. (2010). A Stochastic EM type algorithm for estimation in data with ascertainment on continuous outcomes. International Journal of Biostatistics 6(1), Article 23.

56. Grünewald, M. and Hössjer, O. (2010). Efficient ascertainment schemes for maximum likelihood estimation. Journal of Statistical Planning and Inference 140(7), 2078-2088.

55. Björkwall, S., Hössjer, O. and Ohlsson, E. (2010). Bootstrapping the separation method in claims reserving. ASTIN Bulletin 40(2), 845-869.

54. Hössjer, O. Hartman, L. and Humphreys, K. (2009). Ancestral recombination graphs under nonrandom ascertainment, with applications to gene mapping. Statistical Applications of Genetics and Molecular Biology 8(1), Article 35.

53. Björkwall, S., Hössjer, O. and Ohlsson, E. (2009). Nonparametric and parametric bootstrap techniques for arbitrary age-to-age development factor methods in stochastic claims reserving. Scandinavian Actuarial Journal 2009(4), 306-331.

52. Eriksson, B., Hössjer, O., Järnmalm, K. and Ohlsson, E. (2009). Assessing individual unexplained variation in non-life insurance. ASTIN Bulletin 39(1), 249-273.

51. Hartman, L., Humphreys, K. and Hössjer, O. (2009). Utilizing identity-by-descent probabilities for genetic fine-mapping in population based samples, via spatial smoothing of haplotype effects. Computational Statistics and Data Analysis 53(5), 1802-1817.

50. Hössjer, O. (2008). On the coefficient of determination for mixed regression models. Journal of Statistical Planning and Inference 138, 3022-3038.

49. Kurbasic, A. and Hössjer, O. (2008). A general method of linkage disequilibrium correction for multipoint linkage and association. Genetic Epidemiology 32, 647-657.

48. Ängquist, L., Hössjer, O. and Groop, L. (2008). Strategies for conditional two-locus nonparametric linkage analysis. Human Heredity 66, 138-156.

47. Hartman-Werner, L. and Hössjer, O. (2008). Fast kriging of large data sets with Gaussian Markov random field models. Computational Statistics and Data Analysis, 52(5), 2331-2349.

46. Sjölander, A., Hartman-Werner, L., Hössjer, O. and Humphreys, K. (2007). Fine mapping of disease genes using tagging SNPs. Annals of Human Genetics 71(6), 815-827.

45. Hössjer, O. (2006). Modelling the effect of inbreeding among founders in linkage analysis.  Theoretical Population Biology 70, 146-163.

44. Kurbasic, A. and Hössjer, O. (2006). Relative risks and effective number of meioses: A unified approach for general genetic models and phenotypes. Annals of Human Genetics 70(6), 907-922.

43. Bengtsson, H. and Hössjer, O. (2006). Methodological study of affine transformations of gene expression data with proposed normalization method. BMC Bioinformatics 2006, 7:100.

42. Anevski, D. and Hössjer, O. (2006). A general asymptotic scheme for inference under order restrictions. Annals of Statistics 34(4), 1874-1930.

41. Frigyesi, A. and Hössjer, O. (2006). Estimating the parameters of the operational model of pharmacological agonism. Statistics in Medicine 25, 2932-2945.

40. Hössjer, O. (2005). Spectral decomposition of score functions in linkage analysis. Bernoulli 1(6), 1093-1113.

39. Hössjer, O. (2005). Combined association and linkage analysis for general pedigrees and genetic models. Statistical Applications of Genetics and Molecular Biology 4(1), Article 11.

38. Johanssson, J-O. and Hössjer, O. (2005). A shot-noise model for paper fibres with  non-uniform orientation. Scandinavian Journal of Statistics 32, 351-363.

37. Hössjer, O. (2005). Information and effective number of meioses in linkage analysis. Journal of Mathematical Biology 50(2), 208-232.

36. Hössjer, O. (2005). Conditional likelihood score functions for mixed models in linkage analysis. Biostatistics 6, 313-332. Supplementary material at http://biostatistics.oupjournals.org/.

35. Ängquist, L. and Hössjer, O. (2005). Improving the calculation of statistical significance in genome-wide scans.  Biostatistics 6(4), 520-538.

34. Ängquist, L. and Hössjer, O. (2004). Using importance sampling to improve simulation in linkage analysis. Statistical Applications of Genetics and Molecular Biology 3(1), Article 5.

33. Kurbasic, A. and Hössjer, O. (2004). On computation of p-values in parametric linkage analysis. Human Heredity 57, 207-219.

32. Hössjer, O. (2003). Asymptotic estimation theory of multipoint linkage analysis under perfect marker information. Annals  of Statistics 31, 1075-1109.

31. Hössjer, O. (2003). Assessing accuracy in linkage analysis by means of confidence regions. Genetic Epidemiology 25, 59-72.

30. Hössjer, O. (2003). Determining inheritance distributions via stochastic penetrances. Journal of the American Statistical Association 98, 1035-1051.

29. Anevski, D. and Hössjer, O. (2002). Monotone regression and density function estimation at a point of discontinuity.  Journal of Nonparametric Statistics 14, 279-294.

28. Frigyesi, A. and Hössjer, O. (2001).  Kernel estimates of dimension spectra for multifractal measures with connections to nonparametric density estimation. Journal of Nonparametric Statistics 13, 351-395.

27. Stromberg, A., Hössjer, O. and Hawkins, D. (2000). The least trimmed differences estimator and alternatives. Journal of the American Statistical Association 95, 853-864.

26. Sköld, M. and Hössjer, O. (1999). On the asymptotic variance of the continuous-time kernel density estimator. Statistics and Probability Letters 44, 97-106. 

25. Opsomer, J., Ruppert, D., Wand, M.P., Holst, U. and Hössjer, O. (1999). Kriging with nonparametric variance function estimation. Biometrics  55, 704-710.

24. Frigyesi, A. and Hössjer, O. (1998).  A test for singularity. Statistics and Probability Letters 40, 215-226.

23. Hössjer, O. (1997). Reqursive U-quantiles. Sequential Analysis 16, 119-129.

22. Ruppert, D., Wand, M., Holst, U. and Hössjer O. (1997). Local polynomial variance function estimation. Technometrics 39, 262-273.

21. Gustafsson, R., Hössjer, O. and Öberg, T. (1997). Adaptive detection of known signals in additive noise by means of kernel density estimators. IEEE Transactions on Information Theory IT 43, 1192-1204.

20. Jones, C. and Hössjer, O. (1996). From basic to reduced bias kernel density estimators: links via Taylor series approximations. Journal of Nonparametric Statistics 7, 23-34.

19. Holst, U., Hössjer, O., Björklund, C., Ragnarsson, P. and Edner, H. (1996). Locally weighted least squares kernel regression and statistical evaluation. Environmetrics 7, 401-416 (with discussion).

18. Hössjer, O. (1996). Asymptotic bias and variance for a general class of varying bandwidth estimators. Probability Theory and Related Fields 105, 159-192.

17. Hössjer, O. (1996). Incomplete generalized L-statistics. Annals of Statistics 24, 2631-2654.

16. Hössjer, O., Rousseeuw, P. and Croux, C. (1996). Asymptotics of an estimator of a robust scale functional. Statistica Sinica 6,375-388.

15. Hössjer, O. and Mielniczuk, J. (1995). Delta method for long-range dependent observations. Journal of Nonparametric Statistics 5, 75-82.

14. Hössjer, O. and Ruppert, D. (1995). Asymptotics for the transformation kernel density estimator. Annals of Statistics 23, 1198-1222.

13. Hössjer, O. (1995). Exact computation of the least trimmed squares estimate in simple linear regression. Computational Statistics and Data Analysis 19, 265-282.

12. Rousseuw, P., Croux, C. and Hössjer, O. (1995). Sensitivity functions and numerical analysis of the repeated median slope. Computational Statistics 10, 71-90.

11. Hössjer, O., Rousseeuw, P. and Ruts, I. (1995). The repeated median intercept estimator: Influence function and asymptotic normality. Journal of Multivariate Analysis 52, 45-72.

10. Hössjer, O. and Holst, U. (1995). On-line density estimators with high efficiency. IEEE Transactions of Information Theory IT-41, 829-833.

9. Hössjer, O., Rousseeuw, P. and Croux, C. (1994). Asymptotics of the repeated median slope estimator. Annals of Statistics 22, 1478-1501.

8. Hössjer, O., Rousseeuw, P. and Croux, C. (1994). Asymptotics of Generalized S-estimators. Journal of Multivariate Analysis 51, 148-177.

7. Hössjer, O. and Croux, C. (1995). Generalizing univariate signed rank statistics for testing and estimating a multivariate location parameter. Journal of Nonparametric Statistics 4, 293-308.

6. Hössjer, O. and Ruppert, D. (1994). Taylor series approximations of transformation kernel density estimators. Journal of Nonparametric Statistics 4, 165-177.

5. Hössjer, O. (1994). Rank-based estimates in the linear model with high breakdown point. Journal of the American Statisitcal Association 89, 149-158.

4. Croux, C., Rousseeuw, P. and Hössjer, O. (1994). Generalized S-estimators. Journal of the American Statistical Association 89, 1271-1281.

3. Hössjer, O. and Mettiji, M. (1993). Robust multiple classification of known signals in additive noise - an asymptotic weak signal approach. IEEE Transactions on Information Theory IT 39, 594-608.

2. Hössjer, O. (1992). On the optimality of S-estimators.  Statistics and Probability Letters 14, 413-419.

1. Hössjer, O. (1991). The change-of-variance function for dependent data. Probability Theory and Related Fields 90, 447-467.

Technical reports

7. Hössjer, O., Nyberg, T., Petrovic, S., Sjöberg, F. and Öberg, T. (2012). Bayesian distributed detection by sensor networks with missing data. Report 2012:6, Mathematical Statistics, Stockholm University.

6. Hössjer, O. (2007). Asymptotics of LR tests at boundary of parameter space under non-identifiability. Report 2007:3, Mathematical Statistics, Stockholm University.

5. Bengtsson, H. and Hössjer, O. (2004). Affine calibration for microarrays with dilution series or spike-ins, Preprint 2004:19, Mathematical Statistics, Lund University.

4. Rosenqvist, A., Hössjer, O. and Holst, J. (1995). Regression diagnostics using residuals and prediction matrix. Preprint 1995:5, University of Lund and Lund Institute of Technology, Dept. of Mathematical Statistics.


3. Rosenqvist, A., Hössjer, O. and Holst, J. (1995). Robust simple regression using the Hough transform. University of Lund and Lund Institute of Technology, Dept. of Mathematical
Statistics, 1995:4.

2. Hössjer, O. and  Ruppert, D. (1993). Transformation kernel density estimators using stochastic bandwidths. Preprint, 1993:12, University of Lund and Lund Institute of Technology, Dept. of Mathematical Statistics.

1. Hössjer, O. (1989). Robust detection of known signals in additive noise. UPTEC-report 89015R, Teknikum, Uppsala university.

Some selected seminars

9. On the use of Markov chains and Perron Frobenuis Theorem in population genetics, June 2015.

8. Quasi equilibrium methods in population genetics, November 2012.

7. Coalescence theory for structured populations with fast migration, April 2011.

6. Gene mapping using coalescence theory, July 2009.

5. Invariance principles and spectral decomposition in genetics, April 2005.

4. Combined association and linkage analysis for general genetic models, March 2005.

3. On multiple testing in bioinformatics and genetics, November 2004.

2. Importance sampling for stochastic processes - with applications to linkage analysis, April 2004.

1. Asymptotic estimation theory of multipoint linkage analysis under perfect marker information. June 2002.

Files

Last updated: December 2, 2018

Bookmark and share Tell a friend