Pith. sign in

Paper Citation Record · LEDGER

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution

As of 19 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:1909.01725.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1909.01725 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:17:29.596158Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

53 of 53 outbound references displayed

  • verified exact0
  • verified fuzzy24
  • unresolved28
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 36b968c9-ed61-46eb-984b-013affa9c6c2 · outbound

This paper cites Finally, in Sec.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Finally, in Sec

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.416057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.349378Z digest=sha256:7ed289d7be4530e7767d90aef495317431417a4e24079c77c831aa999a43acdb

Observation a8b6d2f6-a132-453d-9026-916c8aef2118 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.401043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.355586Z digest=sha256:8c879d55b0d5fdc60893ca404b4932ff9b5fb86bcf4fd4dec949c177fc47338c

Observation 6b16bc70-b29d-4fe3-83dc-bc92c26251ee · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.386072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.360271Z digest=sha256:7def05503cb135b1f527bc06ddfac85d2f33a3ebcf875dfc9e39d57d404ee118

Observation d002a848-3ffa-4ddc-8afb-b777d93990fb · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.371338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.365285Z digest=sha256:8c4bab54f2f5c035039299785d573c8dcc405f999f4b3c313ba33fe8de95e888

Observation 74d477c9-cfa9-4984-977e-2ba283788b9c · outbound

This paper cites In this case, noting that F−1(y) = exp ( m + √ 2s erf−1(2y− 1) ) (Table I), and that Φ(xG) is given in Eq.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution In this case, noting that F−1(y) = exp ( m + √ 2s erf−1(2y− 1) ) (Table I), and that Φ(xG) is given in Eq

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.356900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.369909Z digest=sha256:bf2d3f010fed42e22bc80a960f734747fc5c3bd6e3e5ea91df49832f0a47155a

Observation bec5e534-af0c-4d00-bf7e-5bdef9eaca4a · outbound

This paper cites However, there is no analytical solution of the integral in Eq.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution However, there is no analytical solution of the integral in Eq

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.341910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.374927Z digest=sha256:fe3cf2d0e268af90ebcd3f5c50ea85e9f7bf8fe61ed07112f8e5b6af95479f82

Observation c7cde9e2-5d6b-4daa-8afb-4da715149532 · outbound

This paper cites As can be seen in Table II, the standardized forms of the Weibull and Pareto distributions have shape parameters, δ and ε respectively, which control the tail behavior.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution As can be seen in Table II, the standardized forms of the Weibull and Pareto distributions have shape parameters, δ and ε respectively, which control the tail behavior

Reference 7

Resolution
malformed identifier
raw_fallback, observed 2026-08-14T05:17:30.326069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.379958Z digest=sha256:93c7f408b7f5901add8437275514213fba91dfd4660505a4a0f82e3416433a45

Observation 90d18f1d-bdfb-4221-b496-c32d10372adb · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.309911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.385344Z digest=sha256:b38e3db1b08bc9f12d15afc47cdf2554d5fc19847bcf7a3765f2c1d277241043

Observation ad5f0221-4a93-4cd1-92f4-b2a243932246 · outbound

This paper cites Likenkaer-Hansen, V.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Likenkaer-Hansen, V

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.295539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.389959Z digest=sha256:f068c44a5507fc6865e168c23ff3ce52c43f30e93338b2138b4c3ca57f83e77d

Observation 39c5d4c1-7a66-4b2a-9c95-b6fe10b8116b · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.280979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.394477Z digest=sha256:6f196f17ac15a66acf3d84e54ab33401efc90c0ed543e611d2f179a2840a2324

Observation 94a20628-4999-44a2-92fb-3f4cafb12e20 · outbound

This paper cites Duarte & V.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Duarte & V

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.265255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.399142Z digest=sha256:5a6eb3e23c935c4c6c97148aadcf5f7d8862e51067a86fa1f48386f48dd7e120

Observation cba60814-1ab5-45c4-b0b4-91959d629034 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.250321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.403914Z digest=sha256:5cec0c3cd61c7efd3901f79fa3639a58e8a4d1354783bd7011cbfb507de67c36

Observation 76d55c82-8aeb-4e87-a2d9-f128041fd194 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.236484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.408797Z digest=sha256:d8407e359553fb73c7f6452402a52bcd8366448a90640ee0d6b7377e42e14ad2

Observation 6524b01e-37eb-4080-b55b-dca28c7d361f · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.221125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.413887Z digest=sha256:b8bd3bb2947737ff5e4937c7f40e6b92d66c681c2f8fa56a97b58fa2b34e9c0a

Observation 314cb06e-36b8-4117-acd9-6e143294502a · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.206678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.418461Z digest=sha256:edc31d7495b8af98d4d99ce1ff1978525cd3274e123fa1e664e95e6773f7945f

Observation 58154747-7583-4d96-b78d-78d48f95bb67 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.191521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.422907Z digest=sha256:7328cf6ca8e8e2f58de88473935bee835eefe9b1d9da0bf9323cd9ff8dac010d

Observation 81159177-5f36-48a7-8ea4-62f5922c7fdd · outbound

This paper cites Lovejoy and B.B.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Lovejoy and B.B

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.176720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.427278Z digest=sha256:5802735475de4e86d30b1bc939dc04dcf8988b1a20d30b31e54ec0ba7ec299ec

Observation d8bbac28-d136-4e69-832f-e302b42aae00 · outbound

This paper cites Bartos and I .M.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Bartos and I .M

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.162529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.431648Z digest=sha256:c604962e522e168b13e8e9a153420ae3f9e7170aa118f7b985c88f7ee37aa053

Observation 9ae43a55-26ae-4ff1-988b-b0a0579fd559 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.148265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.436175Z digest=sha256:e0b8a0a0ac6e86793efdf81cbf3ef2652eda07098de12c9a8db7190ece33f7b8

Observation cff3ecee-cdee-4635-82c0-fd17ba79dc13 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.134452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.440518Z digest=sha256:a838fa34fd9f1c7cc8280718cfe870f216b4d9cf2ea50fbf8b08b5f2209b39c9

Observation 0640b4f4-17ca-434e-82cc-8ba8224ab4ab · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 21

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.120782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.445273Z digest=sha256:60f9ff2a118cf5fc1432be2a1592135cf9e2e30396cddccf6079a0686f47ca6f

Observation afb8d357-b986-4b18-b851-f6f9b3965a19 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 22

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.106828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.449618Z digest=sha256:32fd25410cf43976a37dad6cae7ded4c90258ecf8400f127e3113e165dc333eb

Observation fa2f71e5-5250-4e3e-8b5f-0ad83e1bbb00 · outbound

This paper cites Bernaola-Galvan, J.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Bernaola-Galvan, J

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.092299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.453915Z digest=sha256:b3dbd68acf55e85886724e458489b135fdef2783b6b99e85422cd70c43f88db3

Observation 9a6b2a37-2e24-4b89-be88-d8a95d0defdf · outbound

This paper cites Theiler, S.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Theiler, S

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.077596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.458141Z digest=sha256:5545554647d89c2b07c121c318b4fbba7d65c328686fbf286211596a1bc7dc5a

Observation 71a59050-da24-4850-a6c8-8e27a04c5986 · outbound

This paper cites Schreiber and A.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Schreiber and A

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.063229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.462462Z digest=sha256:5663df5495ad504edcd0fa9cad96214b50c4ed7d148ecfb039f528b3cbd8fe75

Observation 8312d8ba-6af9-4d70-8b9c-66e4e5e0ad1e · outbound

This paper cites Kugiumtzis.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Kugiumtzis

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.048404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.467594Z digest=sha256:9f4ce036dbc376bf7056b7ab0f045078027f29e29bbfdeac7ab314caa90edb39

Observation 24b36d3d-d543-4b0e-abe0-d10df87f5545 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.033862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.472906Z digest=sha256:c809c06da022e122e37712ef94c6fc51fe18a65c2b249de69599468fe0135e96

Observation fd04899b-1b9b-4c3b-be45-ffcc7ad7aa89 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 28

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:30.019760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.478158Z digest=sha256:30b9568efc8aae650b156a5a1b64dc9e80e028527bd819e415c6b784799d1095

Observation 1d23446f-b01d-4174-97cf-3643e2b5770e · outbound

This paper cites Press, S.A.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Press, S.A

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:30.005247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.482648Z digest=sha256:a6910f293cca5d7bee6431264e56fe6fd0a726daf14442f2c1fc7048eef40ee4

Observation bb746aae-bb45-4aeb-aa9d-2b4dc6ebc880 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.991043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.487784Z digest=sha256:795228c38f6dd4861f29536c8dbe0347c632ff72180712c19a3da7c4e067d53c

Observation 977a9343-47bf-47c1-968a-eeb27e5e65d0 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 31

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.977173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.492172Z digest=sha256:679548898112fad728d08685689e4bf4a5991bed7344a649198664dfc0e1578d

Observation 88af1d41-22cf-4b1b-b25b-5540a920a243 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 32

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.962342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.496692Z digest=sha256:c0226741f2e4345787820143397e24c26b62e803ef8d7bdd719b503bbbae1d3a

Observation e8e1a0eb-c945-4ab1-a90e-7d0aa4b9d150 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 33

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.947164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.501671Z digest=sha256:662d5334050cb11b191c9fc6706f1b99cfd7eb3ce79031da5f0356f9fd0515a6

Observation cc1bd0f2-5546-4400-a894-ca329d2541ed · outbound

This paper cites Kugiumtzis and E.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Kugiumtzis and E

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.932163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.505988Z digest=sha256:eaba404d8ea1a55d00676d56cbeed5a4547ab475c61f3fdf2f74446d94058f04

Observation 658a5abf-feba-4938-91d2-af84b6dabb04 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.916398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.511268Z digest=sha256:c5eb40ec890c10ca0de30a11e2cc96838f5a00e74c12e315d82fbd338945aba3

Observation 9fe4503d-8941-405c-9992-ccec1d48e6ed · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.901681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.515566Z digest=sha256:4b71d5cf11557d32aacd556aa7ac643b9206d6101ae2f0d735c1ff3dab7543c5

Observation bbe2d041-d98d-4b51-a946-05b39f1a9ef4 · outbound

This paper cites Ng and M.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Ng and M

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.885265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.520371Z digest=sha256:dc8f0066e1e2fb92c774a9fbf435c79651d0f4433abb14ea42dbdd77eb91496e

Observation a159aa1e-e4ff-45be-96b0-35c3a592a6e6 · outbound

This paper cites Kugiumtzis.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Kugiumtzis

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.869573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.524888Z digest=sha256:b3c43078b8010ca07705fcd772cc61eac715e0fe7a99627800d1765a20c9c69d

Observation dccccb61-3f99-40fe-a6b9-d8bd12712f1d · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 39

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.854604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.529443Z digest=sha256:2f288be0240de01ef412c5222ce9c9f3fd94ebc1181f43d16212dfd93d0d1d2c

Observation 3305738d-38b4-454c-b776-d659e4f7eb60 · outbound

This paper cites Statistics for Long-Memory Processes, (Chapman and Hall/CRC, 1998).

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Statistics for Long-Memory Processes, (Chapman and Hall/CRC, 1998)

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.840654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.533845Z digest=sha256:19d565a553aa92600556ea9ea0849c466779cff1b08dc347423e470a7e8d60ca

Observation 7227c845-4a8e-466e-8fdb-eaa5d0287ebe · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.825524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.538445Z digest=sha256:fd6b6198fef4918586eeb50db0c7cfd02138432eb2307f885089b1e5a6e41ad3

Observation a5a8b643-2b9d-4e3b-8a53-18a7b08124e0 · outbound

This paper cites Carretero-Campos, P.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Carretero-Campos, P

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.810529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.543631Z digest=sha256:c0020361f1029e172c93ee638026c09176d83541acd2fd8d1b088f3debad3629

Observation a68ddd4a-69aa-43ac-9022-761501bdcad8 · outbound

This paper cites Kalisky, Y.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Kalisky, Y

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.795392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.548041Z digest=sha256:84c6fb9a152f15e91a0bd7133b16535239353f1c54ff2dd0a586c129fab95c73

Observation e784c22c-bf39-400c-9bdc-aa2b1b2d7e30 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.779155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.552355Z digest=sha256:7fd48d9c0821281314c8ab248902b1dbd7251dba8938ae28516dfe324361533f

Observation 3238f358-ad78-4b74-846c-20828eee8bdb · outbound

This paper cites G´ omez-Extremera, P.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution G´ omez-Extremera, P

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.762882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.557057Z digest=sha256:0cb6eb9b84c88a8947b690dce287ca232ac5ced824c84a39d7bc4aea1f069f71

Observation ac07f20a-d269-460d-95a2-8cc96ba36c6e · outbound

This paper cites Carpena, M.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Carpena, M

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.748191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.562273Z digest=sha256:0edf2dd18354801caa31c137acf02cf59b2bfff83a2117bd1371ad5ce425131a

Observation 02b4c08d-b223-4b92-a43c-e0707e2e8c48 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.733338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.566840Z digest=sha256:03f8c151e414d5703c921affd4444ac3c65516a96754d4d4af39927b3b9b0063

Observation a2e974f8-91ff-42a0-9d7e-0b98c57259ee · outbound

This paper cites Kawasaki, Y.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Kawasaki, Y

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.716957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.571367Z digest=sha256:b323c069d31767696f72a3acdbc25563a4ec5f5a231c151cb6551060ee78f733

Observation a06c340d-260f-4a4d-938e-bf5017dc5768 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.699627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.575789Z digest=sha256:1ad2bbd5c234f1b0e0b719752327cde4f4a2479144c4159e6e6b98509585cf09

Observation 72a08216-8dfc-4a7c-be6b-73fbe6ddeb0b · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 50

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.682881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.581047Z digest=sha256:c40476093f293ab414bd5aabe86075558533d356c944acd7fec2989052920e33

Observation d7f2dc7c-d5df-4c4e-ac3e-56f46cac8b54 · outbound

This paper cites Carpena, P.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Carpena, P

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.668009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.585823Z digest=sha256:5eb64c809c1d21caa593a3a3285f5e03cbb77751198b9e147706da3f44370117

Observation aed4caa7-23a2-406b-933c-0afdff7a018c · outbound

This paper cites Carpena, J.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Carpena, J

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.652377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.590684Z digest=sha256:8bfef2ac22c8514fec8719fca6b95e66cbb106993fbb26ebaf35b36de200fc50

Observation a57e335f-15b7-4ff6-b474-77781c8ad7d4 · outbound

This paper cites Podobnik, D.F.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Podobnik, D.F

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.635358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-14T05:17:29.596158Z digest=sha256:75608f4aaa2b6193bd427e7f91be95e3dbef8b46455028f46dd145e3eafc35de

Pith citing papers

No inbound Pith citation observations are available.