Jens Perch Nielsen

Actuary from Copenhagen and statistician from UC-Berkeley. Worked as appointed actuary in his young days and led various product development departments before specialising in research and development. He became research director of RSA with responsibilities in life as well as non-life in 1999. From 2006 until 2012 he worked as an entreprenuer and he is still co-owner and board member of Copenhagen based ScienceFirst, London based Operational Science and Cyprus based Emergent. He is co-author of more than 100 scientific papers in reviewed journals of actuarial science, economics, econometrics and statistics and also one book on quantitative operational risk modelling and associate editor of Digital Finance. Received 900K in grants 2016-2020 from the Institute of Actuaries to reinvent and communicate pension products.

Memberships of Professional Organisations

• Associated member, Institute of Actuaries, London, Jun 2014 – present
• Fellow, Royal Society of Statistics, London, Aug 2013 – present
• Full qualified member. Former member of board., Danish Institute of Actuarial Science, Jan 1993 – present

Award

University of Copenhagen (2006) Honory Professor (Adjungated professor)

Languages

Danish.

Expertise

Primary Topics

• Risk Management
• Knowledge Management
• Actuarial Statistics
• Annuities
• Financial Econometrics
• Simulation Methods
• Portfolio Choice
• Management Science
• Leadership
• Venture Capital
• Organization TheoryI
• nsurance
• Asset Pricing
• Mathematical Finance
• Small Business Management
• Pension Funds
• Actuarial Science
• Entrepreneurship
• Quantitative Finance
• Econometric & Statistical Methods
• Statistics
• Bond Markets
• Risk Modelling
• Finance
• Demography
• Econometrics
• Asset Valuation

Industries/Professions

• financial services
• insurance

Geographic Areas

• Europe - Western
• Scandinavia

Research

Three main area of research are 1) In-Sample Forecasting (developed by Cass academics and co-auhtors) 2) Defined benefit advantages adapted to defined contribution products (implementing Merton's vision) 3) Asbestos mortality forecasting (application of In-Sample Forecasting).

Research Topics

• In-Sample Forecasting General forecasting for longevity and reserving as well as many biostatistical forecasting problems
• Prediction of stock returns Take advantage of state-of-the-art smoothing methods while predicting stock returns
• Bandwidth selection The fundamental problem of mathematical statistics: the variance/Bias trade off.
• Estimation of outstanding liabilities. A major non-life actuarial issue. We focus on introducing state-of-the-art methodology of mathematical statistics into this old actuarial theme.
• Asbestos mortality forecasting Application of In-Sample forecasting
• Remember Defined benefits methodology when moving to defined contribution pensions new pension products adapted to current market situation

Book

• Bolance, C., Guillen, M., Gustafsson, J. and Nielsen, J.P. (2012). Quantitative Operational Risk Models. Chapman and Hall/CRC Finance Series. ISBN 978-1-4398-9592-4.
• ## Chapters (5)
• Donnelly, C., Guillen, M. and Nielsen, J.P. (2016). Fundamentals of Cost and Risk that Matter to Pension Savers and Life Annuitants. In Mitchell, O.S. and Maurer, R. (Eds.), Retirement System Risk Management Implications of the New Regulatory Order (pp. 171–185). Oxford University Press. ISBN 978-0-19-251232-1.
• Miranda, M.D.M., Nielsen, J.P. and Sperlich, S. (2009). One Sided Crossvalidation for Density Estimation. In Gregoriou, G.N. (Ed.), Operational Risk Towards Basel III: Best Practices and Issues in Modeling, Management and Regulation (pp. 177–196). New Jersey: John Wiley and Sons.
• Bolance, C., Guillen, M. and Nielsen, J.P. (2009). Transformation Kernel Estimation of insurance cost claim distributions. In Corazza, M. and Pizzi, C. (Eds.), Mathematical and statistical methods for actuarial science and finance (pp. 223–231). Springer.
• Nielsen, J.P. and Haastrup, S. (1998). The historical perspective of the Danish actuarial profession. Transactions of the 26th International Congress of Actuaries (pp. 193–200).
• Nielsen, J.P. and Voldsgaard, P. (1996). Structured nonparametric marker dependent hazard estimation: An application to health dependent mortality. Proceedings of 27th Astin Conference in Copenhagen (pp. 634–641).

Journal Articles (114)

• Bräutigam, M., Guillén, M. and Nielsen, J.P. (2017). Facing Up to Longevity with Old Actuarial Methods: A Comparison of Pooled Funds and Income Tontines. Geneva Papers on Risk and Insurance: Issues and Practice, 42(3), pp. 406–422. doi:10.1057/s41288-017-0056-1.
• Lee, Y.K., Mammen, E., Nielsen, J.P. and Park, B.U. (2017). Operational time and in-sample density forecasting. Annals of Statistics, 45(3), pp. 1312–1341. doi:10.1214/16-AOS1486.
• Hiabu, M., Mammen, E., Martìnez-Miranda, M.D. and Nielsen, J.P. (2016). In-sample forecasting with local linear survival densities. Biometrika, 103(4), pp. 843–859. doi:10.1093/biomet/asw038.
• Gámiz, M.L., Mammen, E., Miranda, M.D.M. and Nielsen, J.P. (2016). Double one-sided cross-validation of local linear hazards. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 78(4), pp. 755–779. doi:10.1111/rssb.12133.
• Martínez-Miranda, M.D., Nielsen, B. and Nielsen, J.P. (2016). Simple benchmark for mesothelioma projection for Great Britain. Occupational and Environmental Medicine, 73(8), pp. 561–563. doi:10.1136/oemed-2015-103303.
• Scholz, M., Sperlich, S. and Nielsen, J.P. (2016). Nonparametric long term prediction of stock returns with generated bond yields. Insurance: Mathematics and Economics, 69(July 2016), pp. 82–96. doi:10.1016/j.insmatheco.2016.04.007.
• Hiabu, M., Margraf, C., Martínez-Miranda, M.D. and Nielsen, J.P. (2016). Cash flow generalisations of non-life insurance expert systems estimating outstanding liabilities. Expert Systems with Applications, 45, pp. 400–409. doi:10.1016/j.eswa.2015.09.021.
• (2016). The link between classical reserving and granular reserving through double chain ladder and its extensions ‐ Abstract of the London Discussion. British Actuarial Journal, 21(01), pp. 117–133. doi:10.1017/S1357321715000240.
• Haibu, M., Margraf, C., Miranda, M.D.M. and Nielsen, J.P. (2015). The Link Between Classical Reserving and Granular Reserving Through Double Chain Ladder and its Extensions. British Actuarial Journal, 21(1), pp. 97–116. doi:10.1017/S1357321715000288.
• Scholz, M., Nielsen, J.P. and Sperlich, S. (2015). Nonparametric Prediction of Stock Returns Based on Yearly Data: The Long-Term View. Insurance: Mathematics and Economics, 65(November 2015), pp. 143–155. doi:10.1016/j.insmatheco.2015.09.011.
• Hiabu, M., Martínez-Miranda, M.D., Nielsen, J.P., Spreeuw, J., Tanggaard, C. and Villegas, A.M. (2015). Global Polynomial Kernel Hazard Estimation. Revista Colombiana de Estadística, 38(2), pp. 399–411. doi:10.15446/rce.v38n2.51668.
• Nielsen, J.P., Donnelly, C., Gerrard, R. and Montserrat, G. (2015). Less is more: increasing retirement gains by using an upside terminal wealth constraint. Insurance: Mathematics and Economics, 64(September 2015), pp. 259–267. doi:10.1016/j.insmatheco.2015.06.003.
• Kuang, D., Nielsen, B. and Nielsen, J.P. (2015). The geometric chain-ladder. Scandinavian Actuarial Journal, 2015(3), pp. 278–300. doi:10.1080/03461238.2013.821952.
• Nielsen, J.P., Young, K.L., Mammen, E. and Byeong, U.P. (2015). Asymptotics for In-Sample Density Forecasting. Annals of Statistics, 43(2), pp. 620–651. doi:10.1214/14-AOS1288.
• Mammen, E., Martínez Miranda, M.D. and Nielsen, J.P. (2015). In-sample forecasting applied to reserving and mesothelioma mortality. Insurance: Mathematics and Economics, 61, pp. 76–86. doi:10.1016/j.insmatheco.2014.12.001.
• Donnelly, C., Guillén, M. and Nielsen, J.P. (2014). Bringing cost transparency to the life annuity market. Insurance: Mathematics and Economics, 56(1), pp. 14–27. doi:10.1016/j.insmatheco.2014.02.003.
• Mammen, E., Martínez Miranda, M.D., Nielsen, J.P. and Sperlich, S. (2014). Further theoretical and practical insight to the do-validated bandwidth selector. Journal of the Korean Statistical Society, 43(3), pp. 355–365. doi:10.1016/j.jkss.2013.11.001.
• Nielsen, B. and Nielsen, J.P. (2014). Identification and Forecasting in Mortality Models. The Scientific World Journal, 2014, pp. 1–24. doi:10.1155/2014/347043.
• Martínez Miranda, M.D., Nielsen, B. and Nielsen, J.P. (2014). Inference and forecasting in the age-period-cohort model with unknown exposure with an application to mesothelioma mortality. Journal of the Royal Statistical Society. Series A: Statistics in Society . doi:10.1111/rssa.12051.
• Gerrard, R., Guillén, M., Nielsen, J.P. and Pérez-Marín, A.M. (2014). Long-Run Savings and Investment Strategy Optimization. The Scientific World Journal, 2014, pp. 1–13. doi:10.1155/2014/510531.
• Guillén, M., Jarner, S.F., Nielsen, J.P. and Pérez-Marín, A.M. (2014). Risk-Adjusted Impact of Administrative Costs on the Distribution of Terminal Wealth for Long-Term Investment. The Scientific World Journal, 2014, pp. 1–12. doi:10.1155/2014/521074.
• Donnelly, C., Englund, M. and Nielsen, J.P. (2014). The importance of the choice of test for finding evidence of asymmetric information. ASTIN Bulletin, 44(2), pp. 173–195. doi:10.1017/asb.2013.33.
• Donnelly, C., Englund, M., Nielsen, J.P. and Tanggaard, C. (2014). Asymmetric Information, Self-selection, and Pricing of Insurance Contracts: The Simple No-Claims Case. Journal of Risk and Insurance, 81(4), pp. 757–780. doi:10.1111/j.1539-6975.2013.01520.x.
• Nielsen, J.P., Verrall, R., Miranda, M.D.M., Hiabu, M. and Agbeko, T. (2014). Validating the Double Chain Ladder Stochastic Claims Reserving Model. Variance: advancing the science of risk, 8(2), pp. 138–160.
• Spreeuw, J., Nielsen, J.P. and Jarner, S.F. (2013). A nonparametric visual test of mixed hazard models. SORT - Statistics and Operations Research Transactions, 37(2), pp. 153–174.
• Gámiz Pérez, M.L., Janys, L., Martínez Miranda, M.D. and Nielsen, J.P. (2013). Bandwidth selection in marker dependent kernel hazard estimation. Computational Statistics & Data Analysis, 68, pp. 155–169. doi:10.1016/j.csda.2013.06.010.
• Guillén, M., Konicz, A.K., Nielsen, J.P. and Pérez-Marín, A.M. (2013). Do not pay for a Danish interest guarantee. The law of the triple blow. Annals of Actuarial Science, 7(02), pp. 192–209. doi:10.1017/S1748499512000176.
• Martinez-Miranda, M.D., Nielsen, J.P., Verrall, R. and Wüthrich, M.V. (2013). Double chain ladder, claims development inflation and zero-claims. Scandinavian Actuarial Journal, 2015(5), pp. 383–405. doi:10.1080/03461238.2013.823459.
• Thuring, F., Nielsen, J.P., Guillén, M. and Bolancé, C. (2013). Segmenting and selecting cross-sale prospects using dynamic pricing. ICORES 2013 - Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems pp. 103–108.
• Martínez Miranda, M.D., Nielsen, J.P., Sperlich, S. and Verrall, R. (2013). Continuous Chain Ladder: Reformulating and generalizing a classical insurance problem. Expert Systems with Applications, 40(14), pp. 5588–5603. doi:10.1016/j.eswa.2013.04.006.
• Martínez-Miranda, M.D., Nielsen, J.P. and Verrall, R. (2013). Double Chain Ladder and Bornhuetter-Ferguson. North American Actuarial Journal, 17(2), pp. 101–113. doi:10.1080/10920277.2013.793158.
• Kaishev, V.K., Nielsen, J.P. and Thuring, F. (2013). Optimal customer selection for cross-selling of financial services products. Expert Systems with Applications, 40(5), pp. 1748–1757. doi:10.1016/j.eswa.2012.09.026.
• Bolancé, C., Guillén, M., Gustafsson, J. and Nielsen, J.P. (2013). Adding prior knowledge to quantitative operational risk models. Journal of Operational Risk, 8(1), pp. 17–32.
• Gámiz Pérez, M.L., Martínez Miranda, M.D. and Nielsen, J.P. (2013). Smoothing survival densities in practice. Computational Statistics and Data Analysis, 58(1), pp. 368–382. doi:10.1016/j.csda.2012.09.011.
• Donnelly, C., Guillén, M. and Nielsen, J.P. (2013). Exchanging uncertain mortality for a cost. Insurance: Mathematics and Economics, 52(1), pp. 65–76. doi:10.1016/j.insmatheco.2012.11.001.
• Guillen, M., Nielsen, J.P., Perez-Marin, A.M. and Petersen, K.S. (2013). Performance measurement of pension strategies: a case study of Danish life-cycle products. SCANDINAVIAN ACTUARIAL JOURNAL, 2013(1), pp. 49–68. doi:10.1080/03461238.2010.546138.
• Nielsen, J.P., Gerrard, G. and Baden-Fuller, C. (2013). Can digital technology bring quality financial advice to the masses? .
• Guillén, M., Perch Nielsen, J., Pérez-Marín, A.M. and Petersen, K.S. (2012). Performance measurement of pension strategies: a case study of Danish life cycle products. Scandinavian Actuarial Journal, 2012(4), pp. 258–277. doi:10.1080/03461238.2010.537835.
• Thuring, F., Nielsen, J.P., Guillén, M. and Bolancé, C. (2012). Selecting prospects for cross-selling financial products using multivariate credibility. Expert Systems with Applications, 39(10), pp. 8809–8816. doi:10.1016/j.eswa.2012.02.011.
• Miranda, M.D.M., Nielsen, J.P. and Verrall, R. (2012). Double chain ladder. ASTIN Bulletin, 42(1), pp. 59–76. doi:10.2143/AST.42.1.216071.
• Martínez-Miranda, M.D., Nielsen, J.P. and Wüthrich, M.V. (2012). Statistical modelling and forecasting of outstanding liabilities in non-life insurance. SORT, 36(2), pp. 195–218.
• Guillén, M., Nielsen, J.P., Scheike, T.H. and Pérez-Marín, A.M. (2012). Time-varying effects in the analysis of customer loyalty: A case study in insurance. Expert Systems with Applications, 39(3), pp. 3551–3558. doi:10.1016/j.eswa.2011.09.045.
• Buch-Kromann, T. and Nielsen, J.P. (2012). Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data. Annals of the Institute of Statistical Mathematics, 64(1), pp. 167–192. doi:10.1007/s10463-010-0313-6.
• Nielsen, J.P., Bolance, C., Guillen, M. and Gustafsson, J. (2012). Quantitative modeling of operational risk losses when combining internal and external data sources. Capco Journal of Financial Transformation, 35(4), pp. 179–185.
• Mammen, E., Nielsen, J.P. and Fitzenberger, B. (2011). Generalized linear time series regression. Biometrika, 98(4), pp. 1007–1014. doi:10.1093/biomet/asr044.
• Miranda, M.D.M., Nielsen, B., Perch Nielsen, J. and Verrall, R. (2011). Cash flow simulation for a model of outstanding liabilities based on claim amounts and claim numbers. ASTIN Bulletin, 41(1), pp. 107–129. doi:10.2143/AST.41.1.2084388.
• Mammen, E., Miranda, M.D.M., Nielsen, J.P. and Sperlich, S. (2011). Do-validation for Kernel density estimation. Journal of the American Statistical Association, 106(494), pp. 651–660. doi:10.1198/jasa.2011.tm08687.
• Kuang, D., Nielsen, B. and Perch Nielsen, J. (2011). Forecasting in an Extended Chain-Ladder-Type Model. Journal of Risk and Insurance, 78(2), pp. 345–359. doi:10.1111/j.1539-6975.2010.01395.x.
• Linton, O., Mammen, E., Nielsen, J.P. and Van Keilegom, I. (2011). Nonparametric regression with filtered data. Bernoulli, 17(1), pp. 60–87. doi:10.3150/10-BEJ260.
• Buch-Kromann, T., Guillén, M., Linton, O. and Nielsen, J.P. (2011). Multivariate density estimation using dimension reducing information and tail flattening transformations. Insurance: Mathematics and Economics, 48(1), pp. 99–110. doi:10.1016/j.insmatheco.2010.10.002.
• Verrall, R., Nielsen, J.P. and Jessen, A.H. (2010). Prediction of RBNS and IBNR claims using claim amounts and claim counts. ASTIN Bulletin, 40(2), pp. 871–887. doi:10.2143/AST.40.2.2061139.
• Nielsen, J.P., Poulsen, R. and Mumford, P. (2010). Capital Allocation for Insurance Companies: Issues and Methods. Belgian Actuarial Bulletin, 9(1), pp. 01–Jul.
• Gustafsson, J., Hagmann, M., Nielsen, J.P. and Scaillet, O. (2009). Local transformation kernel density estimation of loss distributions. Journal of Business and Economic Statistics, 27(2), pp. 161–175. doi:10.1198/jbes.2009.0011.
• Linton, O., Nielsen, J.P. and Nielsen, S.F. (2009). Non-parametric regression with a latent time series. Econometrics Journal, 12(2), pp. 187–207. doi:10.1111/j.1368-423X.2009.00278.x.
• Englund, M., Gustafsson, J., Nielsen, J.P. and Thuring, F. (2009). Multidimensional credibility with time effects: An application to commercial business lines. Journal of Risk and Insurance, 76(2), pp. 443–453. doi:10.1111/j.1539-6975.2009.01306.x.
• Nielsen, J.P., Tanggaard, C. and Jones, M.C. (2009). Local linear density estimation for filtered survival data, with bias correction. Statistics, 43(2), pp. 167–186. doi:10.1080/02331880701736648.
• Kuang, D., Nielsen, B. and Nielsen, J.P. (2009). Chain-Ladder as Maximum Likelihood Revisited. Annals of Actuarial Science, 4(01), pp. 105–121. doi:10.1017/S1748499500000610.
• Guillen, M., Nielsen, J.P. and Perez-Marin, A.M. (2009). Compra cruzada y fidelidad del cliente en el sector asegurador. Esic Market, 132, pp. 107–136.
• Guillen, M., Nielsen, J.P. and P�rez Marin, A.M. (2009). Cross-buying behaviour and customer loyalty in the insurance sector. Esic Market, 2009(January - April), pp. 77–105.
• Gustafsson, J., Hagmann, M., Nielsen, J.P. and Scaillet, O. (2009). Transformation Kernel Density Estimation of Loss Distributions. Journal of Business and Economic Statistics, 27, pp. Jan–15.
• Guillen, M., Gustafsson, J. and Perch Nielsen, J. (2008). Combining underreported internal and external data for operational risk measurement. The Journal of Operational Risk, 3(4), pp. 3–24. doi:10.21314/JOP.2008.050.
• Kuang, D., Nielsen, B. and Nielsen, J.P. (2008). Identification of the age-period-cohort model and the extended chain-ladder model. Biometrika, 95(4), pp. 979–986. doi:10.1093/biomet/asn026.
• Kuang, D., Nielsen, B. and Nielsen, J.P. (2008). Forecasting with the age-period-cohort model and the extended chain-ladder model. Biometrika, 95(4), pp. 987–991. doi:10.1093/biomet/asn038.
• Bolancé, C., Guillén, M. and Nielsen, J.P. (2008). Inverse beta transformation in kernel density estimation. Statistics and Probability Letters, 78(13), pp. 1757–1764. doi:10.1016/j.spl.2008.01.028.
• Gustafsson, J. and Perch Nielsen, J. (2008). A mixing model for operational risk. The Journal of Operational Risk, 3(3), pp. 25–37. doi:10.21314/JOP.2008.049.
• Guillén, M., Høgh, N., Nielsen, J.P. and Pérez-Marín, A.M. (2008). Froot and Stein Revisited Once Again. Annals of Actuarial Science, 3(1-2), pp. 121–126. doi:10.1017/S1748499500000488.
• Brockett, P.L., Golden, L.L., Guillen, M., Nielsen, J.P., Parner, J. and Perez-Marin, A.M. (2008). Survival Analysis of a Household Portfolio of Insurance Policies: How Much Time Do You Have to Stop Total Customer Defection? Journal of Risk & Insurance, 75(3), pp. 713–737. doi:10.1111/j.1539-6975.2008.00281.x.
• Englund, M., Guillén, M., Gustafsson, J., Nielsen, L.H. and Nielsen, J.P. (2008). Multivariate latent risk: A credibility approach. ASTIN Bulletin, 38(1), pp. 137–146. doi:10.2143/AST.38.1.2030406.
• Guillen, M., Nielsen, J.P. and Pérez-Marín, A.M. (2008). The need to monitor customer loyalty and business risk in the European insurance industry. Geneva Papers on Risk and Insurance: Issues and Practice, 33(2), pp. 207–218. doi:10.1057/gpp.2008.1.
• Mammen, E. and Nielsen, J.P. (2007). A general approach to the predictability issue in survival analysis with applications. Biometrika, 94(4), pp. 873–892. doi:10.1093/biomet/asm062.
• Buch-Kromann, T., Englund, M., Gustafsson, J., Perch Nielsen, J. and Thuring, F. (2007). Non-parametric estimation of operational risk losses adjusted for under-reporting. Scandinavian Actuarial Journal, 2007(4), pp. 293–304. doi:10.1080/03461230701642471.
• Guillen, M., Gustafsson, J., Nielsen, J.P. and Pritchard, P. (2007). Using External Data in Operational Risk. The Geneva Papers on Risk and Insurance Issues and Practice, 32(2), pp. 178–189.
• GUILLEN, M.O.N.T.S.E.R.R.A.T., NIELSEN, J.E.N.S.P. and PEREZ-MARIN, A.N.A.M. (2007). Improving the Efficiency of the Nelson-Aalen Estimator: the Naive Local Constant Estimator. Scandinavian Journal of Statistics, 34(2), pp. 419–431.
• Guillen, M., Nielsen, J.P. and Perez-Marin, A.M. (2006). Multiplicative Hazard Models for Studying the Evolution of Mortality. Annals of Actuarial Science, 1(01), pp. 165–177. doi:10.1017/S1748499500000099.
• Gustafsson, J., Perch Nielsen, J., Pritchard, P. and Roberts, D. (2006). Quantifying operational risk guided by kernel smoothing and continuous credibility: A practitioner's view. The Journal of Operational Risk, 1(1), pp. 43–55. doi:10.21314/JOP.2006.005.
• Høgh, N., Linton, O. and Nielsen, J.P. (2006). The Froot-Stein Model Revisited. Annals of Actuarial Science, 1(01), pp. 37–47. doi:10.1017/S174849950000004X.
• Guillen, M., Nielsen, J.P. and Perez Marin, A. (2006). La gestion seguradora bajo el enfoque del multicontrado. Revista Espana de Seguros, 127(JUL-SEP), pp. 529–539.
• Gustafsson, J., Nielsen, J.P., Pritchard, P. and Roberts, D. (2006). Quantifying Operational Risk Guided by Kernel Smoothing and Continuous Credibility. Journal of Finance Risk Management, 3(2), pp. 23–47.
• Guillen, M., Jorgensen, J.P.N. and Nielsen, J. (2006). Return smoothing mechanisms in life and pension insurance: Path dependent contingent claims. Insurance: Mathematics and Economics, 38(2), pp. 229–258. doi:10.1016/j.insmatheco.2005.06.014.
• Nielsen, J.P., Montserrat, G. and Pérez-Marín, A. (2006). La duración de distintos contratos de seguros en los hogares. Un enfoque integrado. Gerencia de Riesgos y Seguros, 96(4), pp. 23–32.
• Buch-larsen, T., Nielsen, J.P., Guillén, M. and Bolancé, C. (2005). Kernel density estimation for heavy-tailed distributions using the champernowne transformation. Statistics, 39(6), pp. 503–516. doi:10.1080/02331880500439782.
• Nielsen, J.P. and Sperlich, S. (2005). Smooth backfitting in practice. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(1), pp. 43–61. doi:10.1111/j.1467-9868.2005.00487.x.
• Nielsen, , Perch, J. and Sandqvist, B. (2005). Proportional hazard estimation adjusted by continuous credibility. Astin Bulletin, 1, pp. 239–258.
• Fledelius, P., Guillen †, M., Nielsen ‡, J.P. and Vogelius §, M. (2004). Two-dimensional Hazard Estimation for Longevity Analysis. Scandinavian Actuarial Journal, 2004(2), pp. 133–156. doi:10.1080/034612301101016516.
• Fledelius, P., Guillen, M., Nielsen, J.P. and Petersen, K.S. (2004). A comparative study of parametric and nonparametric estimators of old-age mortality in Sweden. Journal of Actuarial Practice, 11, pp. 103–128.
• Fledelius, P., Lando, D. and Nielsen, J.P. (2004). Nonparametric analyses of rating transitions and default data. Journal of Investment Management (JOIM), 2(2), pp. 71–85.
• Nielsen, J.P. and Sperlich, S. (2003). Prediction of Stock Returns: A New Way to Look at It. ASTIN Bulletin, 33(02), pp. 399–417. doi:10.1017/S0515036100013532.
• Nielsen, J.P. (2003). Variable bandwidth kernel hazard estimators. Journal of Nonparametric Statistics, 15(3), pp. 355–376. doi:10.1080/1048525031000120260.
• Linton, O., Nielsen, J.P. and Geer, S.V.D. (2003). Estimating multiplicative and additive marker dependent hazard functions by backfitting with the assistance of marginal integration. Annals of Statistics, 23(2), pp. 464–492.
• Nielsen, J.P. (2003). Smoothing and Prediction with a View to Actuarial Science, Biostatistics and Finance. Scandinavian Actuarial Journal, 2003(1), pp. 51–74. doi:10.1080/03461230308484.
• Bolancé, C., Guillen, M. and Nielsen, J.P. (2003). Kernel density estimation of actuarial loss functions. Insurance: Mathematics and Economics, 32(1), pp. 19–36. doi:10.1016/S0167-6687(02)00191-9.
• Mammen, , Enno, and Nielsen, J.P. (2003). Generalised Structured Models. Biometrika, 90(3), pp. 551–566.
• Nielsen, J.P. and Tanggaard, C. (2001). Boundary and Bias Correction in Kernel Hazard Estimation. Scandinavian Journal of Statistics, 28(4), pp. 675–698. doi:10.1111/1467-9469.00262.
• Felipe, A., Guillen, M. and Nielsen, J.P. (2001). Longevity studies based on kernel hazard estimation. Insurance: Mathematics and Economics, 28(2), pp. 191–204. doi:10.1016/S0167-6687(00)00076-7.
• Nielsen, , Perch, J. and Tanggaard, C. (2001). Simple boundary and bias correction in kernel density estimation. Scandinavian Journal of Statistics, 28, pp. 695–724.
• Linton, O., Enno Mammen, J.P.N., Tanggaard, C. and Nielsen, J. (2001). Yield curve estimation by kernel smoothing methods. Journal of Econometrics, 105, pp. 191–204.
• Nielsen, J.P. and Sandqvist, B.L. (2000). Credibility Weighted Hazard Estimation. ASTIN Bulletin, 30(02), pp. 405–417. doi:10.2143/AST.30.2.504643.
• Nielsen, J.P. (2000). Super-Efficient Prediction Based on High-Quality Marker Information. ASTIN Bulletin, 30(02), pp. 295–303. doi:10.2143/AST.30.2.504636.
• Yang, L., Hardle, W. and Nielsen, J. (1999). Nonparametric Autoregression with Multiplicative Volatility and Additive mean. Journal of Time Series Analysis, 20(5), pp. 579–604. doi:10.1111/1467-9892.00159.
• Nielsen, J. (1999). Super-efficient hazard estimation based on high-quality marker information. Biometrika, 86(1), pp. 227–232. doi:10.1093/biomet/86.1.227.
• Neilsen, J.P. (1999). Multivariate Boundary Kernels from Local Linear Estimation. Scandinavian Actuarial Journal, 1999(1), pp. 93–95. doi:10.1080/03461230050131902.
• Mammen, E., Linton, O. and Nielsen, J.P. (1999). The existence and asymptotic properties of a backfitting algorithm under weak conditions. Annals of Statistics, 27, pp. 1443–1490.
• Nielsen, J.P. (1998). Marker dependent kernel hazard estimation from local linear estimation. Scandinavian Actuarial Journal, 1998(2), pp. 113–124. doi:10.1080/03461238.1998.10413997.
• Linton, , Oliver, and Nielsen, J.P. (1998). An optimization interpretation of integration and backfitting estimators for seperable nonparametric models. Journal of Royal Statistical Society, Series B, 60, pp. 217–222.
• Nielsen, J.P. (1998). Multiplicative bias correction in kernel hazard estimation. Scandinavian Journal of Statistics, 25, pp. 541–553.
• Nielsen, J.P., Linton, O. and Bickel, P. (1998). On a semiparametric survival model with flexible covariate effect. Annals of Statistics, 26(1), pp. 215–241.
• LINTON, O. and NIELSEN, J.P. (1995). A kernel method of estimating structured nonparametric regression based on marginal integration. Biometrika, 82(1), pp. 93–100. doi:10.1093/biomet/82.1.93.
• JONES, M.C., LINTON, O. and NIELSEN, J.P. (1995). A simple bias reduction method for density estimation. Biometrika, 82(2), pp. 327–338. doi:10.1093/biomet/82.2.327.
• Nielsen, , Perch, J. and Linton, O. (1995). Kernel Estimation in a Nonparametric Marker Dependent Hazard Model. Annals of Statistics, 23(5), pp. 1735–1748.
• Linton, O. and Nielsen, J.P. (1994). A multiplicative bias reduction method for nonparametric regression. , 19(3), pp. 181–187.
• Malani, H.M., Redfearn, W.J. and Nielsen, J.P. (1993). A note on the asymptotic variance of survival function in the semi-Markov model. , 18(1), pp. 19–25.
• Fusaro, R.E., Nielsen, J.P. and Scheike, T.H. (1993). Marker-dependent hazard estimation: An application to AIDS. Statistics in Medicine, 12(9), pp. 843–865. doi:10.1002/sim.4780120905.
• JEWELL, N.I.C.H.O.L.A.S.P. and NIELSEN, J.E.N.S.P. (1993). A framework for consistent prediction rules based on markers. Biometrika, 80(1), pp. 153–164. doi:10.1093/biomet/80.1.153.
• Nielsen, J.P. The Future of Financial Planning and Fund Distribution: Entering the Digital Age. Shorex .

Editorial Activities (23)

• Journal of Digital Finance, Associate Editor, 2015 – present.
• PlosOne, Referee, 2015 – present.
• Risk, Referee, 2015 – present.
• European Journal of Operational Research, Referee, 2014 – present.
• Insurance: Mathematics and Economics, Referee, 2013 – present.
• Journal of Econometrics, Referee, 2013 – present.
• Journal of Time Series, Referee, 2013 – present.
• Geneva Papers, Referee, 2012 – present.
• Astin Bulletin, Referee, 2011 – present.
• Annals of Actuarial Science, Referee, 2010 – present.
• Statistics and Probability Letters, Referee, 2009.
• Annals of Institute of Mathematical Statistics, Referee, 2009 – present.
• Canadian Journal of Statistics, Referee, 2009 – present.
• Econometrika, Referee, 2004 – 2006.
• Journal of Econometrics, Referee, 2004.
• Statistics, Referee, 2003 – 2009.
• Biometrical Journal, Referee, 2002.
• Journal of Royal Statistical Society Series B, Referee, 2000 – 2006.
• Biometrika, Referee, 2000 – present.
• JASA, Referee, 2000 – present.
• Annals of Statistics, Referee, 1995 – present.
• Scandinavian Actuarial Journal, Referee, 1995 – present.
• Scandinavian Journal of Statistics, Referee, 1995 – present.